diff --git a/CHANGELOG.md b/CHANGELOG.md
index df04e029..6de75eac 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # @vladmandic/human  
 
-Version: **1.1.10**  
+Version: **1.2.0**  
 Description: **Human: AI-powered 3D Face Detection, Face Embedding & Recognition, Body Pose Tracking, Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction & Gesture Recognition**  
 
 Author: **Vladimir Mandic <mandic00@live.com>**  
@@ -9,6 +9,10 @@ Repository: **<git+https://github.com/vladmandic/human.git>**
 
 ## Changelog
 
+### **1.1.11** 2021/03/21 mandic00@live.com
+
+- refactor face classes
+
 ### **1.1.10** 2021/03/18 mandic00@live.com
 
 - cleanup
diff --git a/demo/browser.js b/demo/browser.js
index 75822e80..5f9ee7f3 100644
--- a/demo/browser.js
+++ b/demo/browser.js
@@ -102,8 +102,8 @@ async function calcSimmilariry(result) {
       document.getElementById('compare-canvas').getContext('2d').drawImage(original.canvas, 0, 0, 200, 200);
     }
   }
-  const simmilarity = human.simmilarity(original?.face[0]?.embedding, result?.face[0]?.embedding);
-  document.getElementById('simmilarity').innerText = `simmilarity: ${Math.trunc(1000 * simmilarity) / 10}%`;
+  const similarity = human.similarity(original?.face[0]?.embedding, result?.face[0]?.embedding);
+  document.getElementById('similarity').innerText = `similarity: ${Math.trunc(1000 * similarity) / 10}%`;
 }
 
 // draws processed results and starts processing of a next frame
diff --git a/demo/embedding.html b/demo/embedding.html
index d6a6309f..c0f4f608 100644
--- a/demo/embedding.html
+++ b/demo/embedding.html
@@ -27,7 +27,8 @@
     <div id="images"></div>
     <br>Selected Face (Enhanced):<br>
     <canvas id="orig" style="width: 200px; height: 200px;"></canvas>
-    <br><br>Extracted Faces - click on a face to sort by simmilarity and get a known face match:<br>
+    <span id="desc" style="visibility: hidden; font-size: 0.4rem;"></span>
+    <br><br>Extracted Faces - click on a face to sort by similarity and get a known face match:<br>
     <div id="faces"></div>
   </body>
 </html>
diff --git a/demo/embedding.js b/demo/embedding.js
index bfaec7d0..97748bc6 100644
--- a/demo/embedding.js
+++ b/demo/embedding.js
@@ -12,9 +12,10 @@ const userConfig = {
     mesh: { enabled: true },
     embedding: { enabled: true },
     iris: { enabled: false },
-    age: { enabled: true },
-    gender: { enabled: true },
+    age: { enabled: false },
+    gender: { enabled: false },
     emotion: { enabled: false },
+    description: { enabled: true },
   },
   hand: { enabled: false },
   gesture: { enabled: false },
@@ -40,41 +41,52 @@ async function analyze(face) {
   // if we have face image tensor, enhance it and display it
   if (face.tensor) {
     const enhanced = human.enhance(face);
+    // const desc = document.getElementById('desc');
+    // desc.innerText = `{"name":"unknown", "source":"${face.fileName}", "embedding":[${face.embedding}]},`;
+    navigator.clipboard.writeText(`{"name":"unknown", "source":"${face.fileName}", "embedding":[${face.embedding}]},`);
     if (enhanced) {
       const c = document.getElementById('orig');
-      const squeeze = enhanced.squeeze();
-      human.tf.browser.toPixels(squeeze, c);
+      const squeeze = enhanced.squeeze().div(255);
+      await human.tf.browser.toPixels(squeeze, c);
       enhanced.dispose();
       squeeze.dispose();
+      const ctx = c.getContext('2d');
+      ctx.font = 'small-caps 0.4rem "Lato"';
+      ctx.fillStyle = 'rgba(255, 255, 255, 1)';
     }
   }
 
   // loop through all canvases that contain faces
   const canvases = document.getElementsByClassName('face');
   for (const canvas of canvases) {
-    // calculate simmilarity from selected face to current one in the loop
-    const simmilarity = human.simmilarity(face.embedding, all[canvas.tag.sample][canvas.tag.face].embedding, 2);
+    // calculate similarity from selected face to current one in the loop
+    const current = all[canvas.tag.sample][canvas.tag.face];
+    const similarity = human.similarity(face.embedding, current.embedding, 2);
     // get best match
-    const person = (simmilarity > 0.99) ? await human.match(face.embedding, db) : { name: '' };
-    // draw the canvas and simmilarity score
-    canvas.title = simmilarity;
-    await human.tf.browser.toPixels(all[canvas.tag.sample][canvas.tag.face].tensor, canvas);
+    const person = await human.match(current.embedding, db);
+    // draw the canvas and similarity score
+    canvas.title = similarity;
+    await human.tf.browser.toPixels(current.tensor, canvas);
     const ctx = canvas.getContext('2d');
     ctx.font = 'small-caps 1rem "Lato"';
     ctx.fillStyle = 'rgba(0, 0, 0, 1)';
-    ctx.fillText(`${(100 * simmilarity).toFixed(1)}% ${person.name}`, 3, 23);
+    ctx.fillText(`${(100 * similarity).toFixed(1)}%`, 3, 23);
     ctx.fillStyle = 'rgba(255, 255, 255, 1)';
-    ctx.fillText(`${(100 * simmilarity).toFixed(1)}% ${person.name}`, 4, 24);
+    ctx.fillText(`${(100 * similarity).toFixed(1)}%`, 4, 24);
+    ctx.font = 'small-caps 0.8rem "Lato"';
+    ctx.fillText(`${current.age}y ${(100 * current.genderConfidence).toFixed(1)}% ${current.gender}`, 4, canvas.height - 6);
+    ctx.font = 'small-caps 1rem "Lato"';
+    if (person.similarity) ctx.fillText(`${(100 * person.similarity).toFixed(1)}% ${person.name}`, 4, canvas.height - 30);
   }
 
-  // sort all faces by simmilarity
+  // sort all faces by similarity
   const sorted = document.getElementById('faces');
   [...sorted.children]
     .sort((a, b) => parseFloat(b.title) - parseFloat(a.title))
     .forEach((canvas) => sorted.appendChild(canvas));
 }
 
-function faces(index, res, fileName) {
+async function faces(index, res, fileName) {
   all[index] = res.face;
   for (const i in res.face) {
     // log(res.face[i]);
@@ -87,13 +99,19 @@ function faces(index, res, fileName) {
     // mouse click on any face canvas triggers analysis
     canvas.addEventListener('click', (evt) => {
       log('Select:', 'Image:', evt.target.tag.sample, 'Face:', evt.target.tag.face, all[evt.target.tag.sample][evt.target.tag.face]);
-      log('Select:', 'Gender:', all[evt.target.tag.sample][evt.target.tag.face].gender);
       analyze(all[evt.target.tag.sample][evt.target.tag.face]);
     });
     // if we actually got face image tensor, draw canvas with that face
     if (res.face[i].tensor) {
-      human.tf.browser.toPixels(res.face[i].tensor, canvas);
+      await human.tf.browser.toPixels(res.face[i].tensor, canvas);
       document.getElementById('faces').appendChild(canvas);
+      const ctx = canvas.getContext('2d');
+      ctx.font = 'small-caps 0.8rem "Lato"';
+      ctx.fillStyle = 'rgba(255, 255, 255, 1)';
+      ctx.fillText(`${res.face[i].age}y ${(100 * res.face[i].genderConfidence).toFixed(1)}% ${res.face[i].gender}`, 4, canvas.height - 6);
+      const person = await human.match(res.face[i].embedding, db);
+      ctx.font = 'small-caps 1rem "Lato"';
+      if (person.similarity && person.similarity > 0.60) ctx.fillText(`${(100 * person.similarity).toFixed(1)}% ${person.name}`, 4, canvas.height - 30);
     }
   }
 }
@@ -102,8 +120,8 @@ async function process(index, image) {
   return new Promise((resolve) => {
     const img = new Image(128, 128);
     img.onload = () => { // must wait until image is loaded
-      human.detect(img).then((res) => {
-        faces(index, res, image); // then wait until image is analyzed
+      human.detect(img).then(async (res) => {
+        await faces(index, res, image); // then wait until image is analyzed
         log('Add image:', index + 1, image, 'faces:', res.face.length);
         document.getElementById('images').appendChild(img); // and finally we can add it
         resolve(true);
@@ -136,10 +154,14 @@ async function main() {
   let images = dir.filter((img) => (img.endsWith('.jpg') && img.includes('sample')));
 
   // enumerate additional private test images in /private, not includded in git repository
-  res = await fetch('/private/err');
+  res = await fetch('/private/me');
   dir = (res && res.ok) ? await res.json() : [];
-  // images = images.concat(dir.filter((img) => (img.endsWith('.jpg'))));
-  images = dir.filter((img) => (img.endsWith('.jpg')));
+  images = images.concat(dir.filter((img) => (img.endsWith('.jpg'))));
+
+  // enumerate just possible error images, not includded in git repository
+  // res = await fetch('/private/err');
+  // dir = (res && res.ok) ? await res.json() : [];
+  // images = dir.filter((img) => (img.endsWith('.jpg')));
 
   // download and analyze all images
   log('Enumerated:', images.length, 'images');
diff --git a/demo/faces.json b/demo/faces.json
index b8a3086f..640474d0 100644
--- a/demo/faces.json
+++ b/demo/faces.json
@@ -1,102 +1,26 @@
 [
-    {"name":"ai","source":"/assets/human-sample-body.jpg","embedding":[2.632819652557373,-1.8534836769104004,2.0401153564453125,3.813028573989868,5.152552604675293,6.58387565612793,2.4153120517730713,2.082664728164673,0.3327586054801941,0.17205268144607544,5.120293140411377,1.617830753326416,3.1026532649993896,2.7013072967529297,0.1472797989845276,-1.038870930671692,0.3460932970046997,4.156325340270996,-1.9277260303497314,2.797736644744873,-1.2892825603485107,-0.932479739189148,-1.577938199043274,2.96262264251709,0.6901331543922424,0.7790188193321228,3.3546159267425537,4.170460224151611,2.4029643535614014,2.4153995513916016,7.513936996459961,0.760761559009552,3.87839412689209,2.2368433475494385,4.52793025970459,3.0775957107543945,7.378010272979736,0.693946361541748,0.9722083806991577,-0.08031022548675537,0.8972775936126709,3.0948381423950195,-2.927286386489868,2.2278172969818115,1.4562621116638184,3.585336446762085,0.25836098194122314,0.8692380785942078,-1.0305207967758179,4.409884452819824,2.7043652534484863,0.24168598651885986,1.4065028429031372,3.4056718349456787,-2.670849323272705,-0.6984208822250366,1.8843040466308594,3.7271180152893066,-0.6245003938674927,2.029428005218506,1.0139710903167725,-0.1857536882162094,7.380436420440674,-0.6864844560623169,4.08783483505249,2.797520399093628,2.552427291870117,1.3243433237075806,3.0222601890563965,-1.2373747825622559,1.5369131565093994,2.3841617107391357,6.338289260864258,-0.05198868364095688,3.736786365509033,2.097527503967285,2.985576868057251,0.7369013428688049,1.422063946723938,1.7589749097824097,2.2876720428466797,-3.4810562133789062,0.0411430224776268,-1.3324987888336182,5.488070964813232,5.893683910369873,-1.6745970249176025,4.128052234649658,5.796738147735596,-0.47520363330841064,1.6041868925094604,-0.07592896372079849,3.408623695373535,-3.963695764541626,0.8829345703125,1.9032931327819824,-0.03466296195983887,3.512486696243286,0.8704050183296204,1.3959259986877441,3.3144397735595703,3.5231525897979736,2.423771619796753,-1.4420123100280762,-1.1175397634506226,-0.2175596058368683,1.4456498622894287,3.4149553775787354,1.7066714763641357,7.442162036895752,0.19832634925842285,2.695158004760742,-1.9821420907974243,-1.7717232704162598,2.385333776473999,3.5953750610351562,4.657474040985107,3.8053250312805176,-2.5774874687194824,7.2682576179504395,7.651699542999268,2.051459312438965,1.3751811981201172,1.5495699644088745,2.8508734703063965,1.8448424339294434,4.445915222167969,5.99959135055542]},
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 ]
\ No newline at end of file
diff --git a/demo/index.html b/demo/index.html
index c8f4ca29..507d334c 100644
--- a/demo/index.html
+++ b/demo/index.html
@@ -93,7 +93,7 @@
     </div>
     <div id="compare-container" style="display: none" class="compare-image">
       <canvas id="compare-canvas" width="200px" height="200px"></canvas>
-      <div id="simmilarity"></div>
+      <div id="similarity"></div>
     </div>
     <div id="samples-container" class="samples-container"></div>
     <div id="log" class="log"></div>
diff --git a/models/faceres.bin b/models/faceres.bin
new file mode 100644
index 00000000..cabc7140
Binary files /dev/null and b/models/faceres.bin differ
diff --git a/models/faceres.json b/models/faceres.json
new file mode 100644
index 00000000..7b22bafa
--- /dev/null
+++ b/models/faceres.json
@@ -0,0 +1,315 @@
+{
+  "format": "graph-model",
+  "generatedBy": "https://github.com/HSE-asavchenko/HSE_FaceRec_tf",
+  "convertedBy": "https://github.com/vladmandic",
+  "signature":
+  {
+      "inputs": {
+        "input_1": { "name": "input_1", "dtype": "DT_FLOAT", "tensorShape":{"dim":[{"size":"-1"},{"size":"224"},{"size":"224"},{"size":"3"}]} }
+      },
+      "outputs":
+      {
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"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_4_bn/batchnorm_1/sub","shape":[128],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_4_relu/Cast","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_4_relu/Const_1","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_4/convolution/merged_input","shape":[1,1,128,256],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_4_bn/batchnorm_1/sub","shape":[256],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_4_relu/Cast","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_4_relu/Const_1","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_5/depthwise_kernel","shape":[3,3,256,1],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_5_bn/batchnorm_1/mul","shape":[256],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_5_bn/batchnorm_1/sub","shape":[256],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_5_relu/Cast","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_5_relu/Const_1","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_5/convolution/merged_input","shape":[1,1,256,256],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_5_bn/batchnorm_1/sub","shape":[256],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_5_relu/Cast","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_5_relu/Const_1","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_6/depthwise_kernel","shape":[3,3,256,1],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_6_bn/batchnorm_1/mul","shape":[256],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_6_bn/batchnorm_1/sub","shape":[256],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_6_relu/Cast","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_6_relu/Const_1","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_6/convolution/merged_input","shape":[1,1,256,512],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_6_bn/batchnorm_1/sub","shape":[512],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_6_relu/Cast","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_6_relu/Const_1","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_7/depthwise_kernel","shape":[3,3,512,1],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_7_bn/batchnorm_1/mul","shape":[512],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_7_bn/batchnorm_1/sub","shape":[512],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_7_relu/Cast","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_7_relu/Const_1","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_7/convolution/merged_input","shape":[1,1,512,512],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_7_bn/batchnorm_1/sub","shape":[512],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_7_relu/Cast","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_7_relu/Const_1","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_8/depthwise_kernel","shape":[3,3,512,1],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_8_bn/batchnorm_1/mul","shape":[512],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_8_bn/batchnorm_1/sub","shape":[512],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_8_relu/Cast","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_8_relu/Const_1","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_8/convolution/merged_input","shape":[1,1,512,512],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_8_bn/batchnorm_1/sub","shape":[512],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_8_relu/Cast","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_8_relu/Const_1","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_9/depthwise_kernel","shape":[3,3,512,1],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_9_bn/batchnorm_1/mul","shape":[512],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_9_bn/batchnorm_1/sub","shape":[512],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_9_relu/Cast","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_9_relu/Const_1","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_9/convolution/merged_input","shape":[1,1,512,512],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_9_bn/batchnorm_1/sub","shape":[512],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_9_relu/Cast","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_9_relu/Const_1","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_10/depthwise_kernel","shape":[3,3,512,1],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_10_bn/batchnorm_1/mul","shape":[512],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_10_bn/batchnorm_1/sub","shape":[512],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_10_relu/Cast","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_10_relu/Const_1","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_10/convolution/merged_input","shape":[1,1,512,512],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_10_bn/batchnorm_1/sub","shape":[512],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_10_relu/Cast","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_10_relu/Const_1","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_11/depthwise_kernel","shape":[3,3,512,1],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_11_bn/batchnorm_1/mul","shape":[512],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_11_bn/batchnorm_1/sub","shape":[512],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_11_relu/Cast","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_11_relu/Const_1","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_11/convolution/merged_input","shape":[1,1,512,512],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_11_bn/batchnorm_1/sub","shape":[512],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_11_relu/Cast","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_11_relu/Const_1","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_12/depthwise_kernel","shape":[3,3,512,1],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_12_bn/batchnorm_1/mul","shape":[512],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_12_bn/batchnorm_1/sub","shape":[512],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_12_relu/Cast","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_12_relu/Const_1","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_12/convolution/merged_input","shape":[1,1,512,1024],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_12_bn/batchnorm_1/sub","shape":[1024],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_12_relu/Cast","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_12_relu/Const_1","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_13/depthwise_kernel","shape":[3,3,1024,1],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_13_bn/batchnorm_1/mul","shape":[1024],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_13_bn/batchnorm_1/sub","shape":[1024],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_13_relu/Cast","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_dw_13_relu/Const_1","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_13/convolution/merged_input","shape":[1,1,1024,1024],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_13_bn/batchnorm_1/sub","shape":[1024],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_13_relu/Cast","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv_pw_13_relu/Const_1","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"global_pooling/Mean/reduction_indices","shape":[2],"dtype":"int32"}]
+      }
+  ]
+}
\ No newline at end of file
diff --git a/package.json b/package.json
index a9deb31f..dd410cf2 100644
--- a/package.json
+++ b/package.json
@@ -1,6 +1,6 @@
 {
   "name": "@vladmandic/human",
-  "version": "1.1.11",
+  "version": "1.2.0",
   "description": "Human: AI-powered 3D Face Detection, Face Embedding & Recognition, Body Pose Tracking, Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction & Gesture Recognition",
   "sideEffects": false,
   "main": "dist/human.node.js",
diff --git a/src/config.ts b/src/config.ts
index 205633c6..4f87c8ae 100644
--- a/src/config.ts
+++ b/src/config.ts
@@ -59,6 +59,11 @@ export interface Config {
       enabled: boolean,
       modelPath: string,
     },
+    description: {
+      enabled: boolean,
+      modelPath: string,
+      skipFrames: number,
+    },
     age: {
       enabled: boolean,
       modelPath: string,
@@ -212,21 +217,14 @@ const config: Config = {
       modelPath: '../models/iris.json',
     },
 
-    age: {
-      enabled: true,
-      modelPath: '../models/age.json',
+    description: {
+      enabled: true,         // to improve accuracy of face embedding extraction it is
+                             // recommended to enable detector.rotation and mesh.enabled
+      modelPath: '../models/faceres.json',
       skipFrames: 31,        // how many frames to go without re-running the detector
                              // only used for video inputs
     },
 
-    gender: {
-      enabled: true,
-      minConfidence: 0.1,    // threshold for discarding a prediction
-      modelPath: '../models/gender.json',
-      skipFrames: 32,        // how many frames to go without re-running the detector
-                             // only used for video inputs
-    },
-
     emotion: {
       enabled: true,
       minConfidence: 0.1,    // threshold for discarding a prediction
@@ -234,9 +232,23 @@ const config: Config = {
       modelPath: '../models/emotion.json',
     },
 
+    age: {
+      enabled: false,        // obsolete, replaced by description module
+      modelPath: '../models/age.json',
+      skipFrames: 31,        // how many frames to go without re-running the detector
+                             // only used for video inputs
+    },
+
+    gender: {
+      enabled: false,        // obsolete, replaced by description module
+      minConfidence: 0.1,    // threshold for discarding a prediction
+      modelPath: '../models/gender.json',
+      skipFrames: 32,        // how many frames to go without re-running the detector
+                             // only used for video inputs
+    },
+
     embedding: {
-      enabled: false,        // to improve accuracy of face embedding extraction it is
-                             // highly recommended to enable detector.rotation and mesh.enabled
+      enabled: false,        // obsolete, replaced by description module
       modelPath: '../models/mobileface.json',
     },
   },
diff --git a/src/faceall.ts b/src/faceall.ts
index a35eaf79..f6ced683 100644
--- a/src/faceall.ts
+++ b/src/faceall.ts
@@ -4,6 +4,7 @@ import * as age from './age/age';
 import * as gender from './gender/gender';
 import * as emotion from './emotion/emotion';
 import * as embedding from './embedding/embedding';
+import * as faceres from './faceres/faceres';
 
 type Tensor = typeof tf.Tensor;
 
@@ -33,6 +34,7 @@ export const detectFace = async (parent, input): Promise<any> => {
   let genderRes;
   let emotionRes;
   let embeddingRes;
+  let descRes;
   const faceRes: Array<{
       confidence: number,
       boxConfidence: number,
@@ -111,11 +113,23 @@ export const detectFace = async (parent, input): Promise<any> => {
       embeddingRes = parent.config.face.embedding.enabled ? await embedding.predict(face, parent.config) : [];
       parent.perf.embedding = Math.trunc(now() - timeStamp);
     }
-    parent.analyze('End Emotion:');
+    parent.analyze('End Embedding:');
+
+    // run emotion, inherits face from blazeface
+    parent.analyze('Start Description:');
+    if (parent.config.async) {
+      descRes = parent.config.face.description.enabled ? faceres.predict(face, parent.config) : [];
+    } else {
+      parent.state = 'run:description';
+      timeStamp = now();
+      descRes = parent.config.face.description.enabled ? await faceres.predict(face.image, parent.config) : [];
+      parent.perf.embedding = Math.trunc(now() - timeStamp);
+    }
+    parent.analyze('End Description:');
 
     // if async wait for results
     if (parent.config.async) {
-      [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);
+      [ageRes, genderRes, emotionRes, embeddingRes, descRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes]);
     }
 
     parent.analyze('Finish Face:');
@@ -134,11 +148,11 @@ export const detectFace = async (parent, input): Promise<any> => {
     // combine results
     faceRes.push({
       ...face,
-      age: ageRes.age,
-      gender: genderRes.gender,
-      genderConfidence: genderRes.confidence,
+      age: descRes.age || ageRes.age,
+      gender: descRes.gender || genderRes.gender,
+      genderConfidence: descRes.genderConfidence || genderRes.confidence,
+      embedding: descRes.descriptor || embeddingRes,
       emotion: emotionRes,
-      embedding: embeddingRes,
       iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,
       angle,
       tensor: parent.config.face.detector.return ? face.image?.squeeze() : null,
diff --git a/src/faceres/faceres.ts b/src/faceres/faceres.ts
new file mode 100644
index 00000000..a3fed331
--- /dev/null
+++ b/src/faceres/faceres.ts
@@ -0,0 +1,146 @@
+import { log } from '../helpers';
+import * as tf from '../../dist/tfjs.esm.js';
+import * as profile from '../profile';
+
+let model;
+let last = { age: 0 };
+let skipped = Number.MAX_SAFE_INTEGER;
+
+type Tensor = typeof tf.Tensor;
+type DB = Array<{ name: string, source: string, embedding: number[] }>;
+
+export async function load(config) {
+  if (!model) {
+    model = await tf.loadGraphModel(config.face.description.modelPath);
+    if (config.debug) log(`load model: ${config.face.description.modelPath.match(/\/(.*)\./)[1]}`);
+  }
+  return model;
+}
+
+export function similarity(embedding1, embedding2, order = 2): number {
+  if (!embedding1 || !embedding2) return 0;
+  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;
+  if (embedding1?.length !== embedding2?.length) return 0;
+  // general minkowski distance, euclidean distance is limited case where order is 2
+  const distance = 4.0 * embedding1
+    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared
+    .reduce((sum, now) => (sum + now), 0) // sum all distances
+    ** (1 / order); // get root of
+  const res = Math.max(0, 100 - distance) / 100.0;
+  return res;
+}
+
+export function match(embedding: Array<number>, db: DB, threshold = 0) {
+  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };
+  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;
+  for (const f of db) {
+    if (f.embedding && f.name) {
+      const perc = similarity(embedding, f.embedding);
+      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };
+    }
+  }
+  return best;
+}
+
+export function enhance(input): Tensor {
+  const image = tf.tidy(() => {
+    // input received from detector is already normalized to 0..1
+    // input is also assumed to be straightened
+    const tensor = input.image || input.tensor || input;
+    /*
+    // do a tight crop of image and resize it to fit the model
+    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right
+    if (!(tensor instanceof tf.Tensor)) return null;
+    const crop = (tensor.shape.length === 3)
+      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing
+      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);
+    */
+    const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model
+
+    /*
+    // convert to black&white to avoid colorization impact
+    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html
+    const [red, green, blue] = tf.split(crop, 3, 3);
+    const redNorm = tf.mul(red, rgb[0]);
+    const greenNorm = tf.mul(green, rgb[1]);
+    const blueNorm = tf.mul(blue, rgb[2]);
+    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);
+    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);
+    */
+
+    /*
+    // optional increase image contrast
+    // or do it per-channel so mean is done on each channel
+    // or do it based on histogram
+    const mean = merge.mean();
+    const factor = 5;
+    const contrast = merge.sub(mean).mul(factor).add(mean);
+    */
+    /*
+    // normalize brightness from 0..1
+    const darken = crop.sub(crop.min());
+    const lighten = darken.div(darken.max());
+    */
+    const norm = crop.mul(255);
+
+    return norm;
+  });
+  return image;
+}
+
+export async function predict(image, config) {
+  if (!model) return null;
+  if ((skipped < config.face.description.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {
+    skipped++;
+    return last;
+  }
+  if (config.videoOptimized) skipped = 0;
+  else skipped = Number.MAX_SAFE_INTEGER;
+  return new Promise(async (resolve) => {
+    // const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);
+    // const enhanced = tf.mul(resize, [255.0]);
+    // tf.dispose(resize);
+    const enhanced = enhance(image);
+
+    let resT;
+    const obj = {
+      age: <number>0,
+      gender: <string>'unknown',
+      genderConfidence: <number>0,
+      descriptor: <number[]>[] };
+
+    if (!config.profile) {
+      if (config.face.description.enabled) resT = await model.predict(enhanced);
+    } else {
+      const profileAge = config.face.description.enabled ? await tf.profile(() => model.predict(enhanced)) : {};
+      resT = profileAge.result.clone();
+      profileAge.result.dispose();
+      profile.run('age', profileAge);
+    }
+    tf.dispose(enhanced);
+
+    if (resT) {
+      tf.tidy(() => {
+        const gender = resT.find((t) => t.shape[1] === 1).dataSync();
+        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;
+        if (confidence > config.face.gender.minConfidence) {
+          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';
+          obj.genderConfidence = Math.min(0.99, confidence);
+        }
+        const age = resT.find((t) => t.shape[1] === 100).argMax(1).dataSync()[0];
+        const all = resT.find((t) => t.shape[1] === 100).dataSync();
+        obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;
+
+        const desc = resT.find((t) => t.shape[1] === 1024);
+        // const reshape = desc.reshape([128, 8]);
+        // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it
+
+        obj.descriptor = [...desc.dataSync()];
+      });
+      resT.forEach((t) => tf.dispose(t));
+    }
+
+    last = obj;
+    resolve(obj);
+  });
+}
diff --git a/src/gender/gender.ts b/src/gender/gender.ts
index 3d3caecb..a76e7a46 100644
--- a/src/gender/gender.ts
+++ b/src/gender/gender.ts
@@ -59,24 +59,39 @@ export async function predict(image, config) {
     enhance.dispose();
 
     if (genderT) {
-      const data = genderT.dataSync();
-      if (alternative) {
-        // returns two values 0..1, bigger one is prediction
-        if (data[0] > config.face.gender.minConfidence || data[1] > config.face.gender.minConfidence) {
-          obj.gender = data[0] > data[1] ? 'female' : 'male';
-          obj.confidence = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);
+      if (!Array.isArray(genderT)) {
+        const data = genderT.dataSync();
+        if (alternative) {
+          // returns two values 0..1, bigger one is prediction
+          if (data[0] > config.face.gender.minConfidence || data[1] > config.face.gender.minConfidence) {
+            obj.gender = data[0] > data[1] ? 'female' : 'male';
+            obj.confidence = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);
+          }
+        } else {
+          // returns one value 0..1, .5 is prediction threshold
+          const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;
+          if (confidence > config.face.gender.minConfidence) {
+            obj.gender = data[0] <= 0.5 ? 'female' : 'male';
+            obj.confidence = Math.min(0.99, confidence);
+          }
         }
+        genderT.dispose();
       } else {
-        // returns one value 0..1, .5 is prediction threshold
-        const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;
+        const gender = genderT[0].dataSync();
+        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;
         if (confidence > config.face.gender.minConfidence) {
-          obj.gender = data[0] <= 0.5 ? 'female' : 'male';
+          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';
           obj.confidence = Math.min(0.99, confidence);
         }
+        /*
+        let age = genderT[1].argMax(1).dataSync()[0];
+        const all = genderT[1].dataSync();
+        age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;
+        const descriptor = genderT[1].dataSync();
+        */
+        genderT.forEach((t) => tf.dispose(t));
       }
     }
-    genderT.dispose();
-
     last = obj;
     resolve(obj);
   });
diff --git a/src/human.ts b/src/human.ts
index de7903e7..c73b3089 100644
--- a/src/human.ts
+++ b/src/human.ts
@@ -6,6 +6,7 @@ import * as faceall from './faceall';
 import * as facemesh from './blazeface/facemesh';
 import * as age from './age/age';
 import * as gender from './gender/gender';
+import * as faceres from './faceres/faceres';
 import * as emotion from './emotion/emotion';
 import * as embedding from './embedding/embedding';
 import * as posenet from './posenet/posenet';
@@ -71,6 +72,7 @@ export class Human {
     emotion: Model | null,
     embedding: Model | null,
     nanodet: Model | null,
+    faceres: Model | null,
   };
   classes: {
     facemesh: typeof facemesh;
@@ -80,6 +82,7 @@ export class Human {
     body: typeof posenet | typeof blazepose;
     hand: typeof handpose;
     nanodet: typeof nanodet;
+    faceres: typeof faceres;
   };
   sysinfo: { platform: string, agent: string };
   perf: any;
@@ -112,6 +115,7 @@ export class Human {
       emotion: null,
       embedding: null,
       nanodet: null,
+      faceres: null,
     };
     // export access to image processing
     // @ts-ignore
@@ -122,6 +126,7 @@ export class Human {
       age,
       gender,
       emotion,
+      faceres,
       body: this.config.body.modelPath.includes('posenet') ? posenet : blazepose,
       hand: handpose,
       nanodet,
@@ -160,19 +165,20 @@ export class Human {
     return null;
   }
 
-  simmilarity(embedding1: Array<number>, embedding2: Array<number>): number {
-    if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);
+  similarity(embedding1: Array<number>, embedding2: Array<number>): number {
+    if (this.config.face.description.enabled) return faceres.similarity(embedding1, embedding2);
+    if (this.config.face.embedding.enabled) return embedding.similarity(embedding1, embedding2);
     return 0;
   }
 
   // eslint-disable-next-line class-methods-use-this
   enhance(input: Tensor): Tensor | null {
-    return embedding.enhance(input);
+    return faceres.enhance(input);
   }
 
   // eslint-disable-next-line class-methods-use-this
-  match(faceEmbedding: Array<number>, db: Array<{ name: string, source: string, embedding: number[] }>, threshold = 0): { name: string, source: string, simmilarity: number, embedding: number[] } {
-    return embedding.match(faceEmbedding, db, threshold);
+  match(faceEmbedding: Array<number>, db: Array<{ name: string, source: string, embedding: number[] }>, threshold = 0): { name: string, source: string, similarity: number, embedding: number[] } {
+    return faceres.match(faceEmbedding, db, threshold);
   }
 
   // preload models, not explicitly required as it's done automatically on first use
@@ -204,6 +210,7 @@ export class Human {
         this.models.posenet,
         this.models.blazepose,
         this.models.nanodet,
+        this.models.faceres,
       ] = await Promise.all([
         this.models.face || (this.config.face.enabled ? facemesh.load(this.config) : null),
         this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),
@@ -214,6 +221,7 @@ export class Human {
         this.models.posenet || (this.config.body.enabled && this.config.body.modelPath.includes('posenet') ? posenet.load(this.config) : null),
         this.models.posenet || (this.config.body.enabled && this.config.body.modelPath.includes('blazepose') ? blazepose.load(this.config) : null),
         this.models.nanodet || (this.config.object.enabled ? nanodet.load(this.config) : null),
+        this.models.faceres || ((this.config.face.enabled && this.config.face.description.enabled) ? faceres.load(this.config) : null),
       ]);
     } else {
       if (this.config.face.enabled && !this.models.face) this.models.face = await facemesh.load(this.config);
@@ -225,6 +233,7 @@ export class Human {
       if (this.config.body.enabled && !this.models.posenet && this.config.body.modelPath.includes('posenet')) this.models.posenet = await posenet.load(this.config);
       if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelPath.includes('blazepose')) this.models.blazepose = await blazepose.load(this.config);
       if (this.config.object.enabled && !this.models.nanodet) this.models.nanodet = await nanodet.load(this.config);
+      if (this.config.face.enabled && this.config.face.description.enabled && !this.models.faceres) this.models.faceres = await faceres.load(this.config);
     }
 
     if (this.#firstRun) {