added benchmarks

Background.md

@@ -0,0 +1,86 @@
+# Comparing Human and FaceAPI
+
+## Background
+
+During 2020 I've modernized `FaceAPI` with goals of using it accross my other ML projects  
+That task turned out to be quite large in scope and resulted in a separate version:
+
+- `FaceAPI`: <https://github.com/vladmandic/face-api>
+
+But after a while, further improvements became impossible due to architecture of models used in `FaceAPI`  
+
+So I've started a new project:
+
+- `Human`: <https://github.com/vladmandic/human>  
+
+With goals of using *SotA* (state-of-the-art) models and expanding detection to cover other parts of any person, not just face while keeping library portable:
+
+- Must be fully asnychronous to support parallel execution
+- Must be able to execute in **Browser** and **NodeJS** environments
+- Must be supported using using `CPU`, `WebGL`, `WASM` and `Node` backends
+- Any used model must have size below 10MB
+- Model can come from any framework: TF, Keras, PyTorch, MLNet, etc.  
+
+<br>
+
+As of `Human` **version 1.0 released in March 2021**, I consider project stable and have fully replaced all use-cases covered by `FaceAPI`:
+
+- Face Detection
+- Face Landmars
+- Age & Gender
+- Emotion
+- Face Description
+
+With improved details - for example:
+
+- FaceAPI has 68-point landmarks while Human has 468-points
+- FaceAPI uses 128-element descriptor while Human uses 1024-element descriptor
+
+While adding functionality:
+
+- Gesture Analysis
+- Body Detection and Landmarks
+- Hand Detection and Landmarks
+
+And expanding platform support:
+
+- *NodeJS* using *WASM*
+- *Browser* with *WebWorkers*
+
+<br><hr>
+
+## Future
+
+I will continue maintaining `FaceAPI` due to popular demand,  
+but all further improvments will be focused on `Human`  
+
+***Any feature requests or model suggestions are welcome!***
+
+<br><hr>
+
+## Benchmarks
+
+Using tensorflow backend (CPU execution using *libtensorflow*)
+
+In most cases CPU will be fully saturated with 4 workers with 80% user-mode and 20% system meaning waiting for I/O such as reading actual images
+
+### System: i5-6500TE; 4-cores @ 2.30GHz
+
+**Enabled: Detection, Landmarks, Age, Gender, Emotion, Descriptor**
+
+- FaceAPI: Processed: 69 images in total: 43896 ms working: 42559 ms average: 616 ms
+- Human: Processed: 69 images in total: 24846 ms working: 23706 ms average: 343 ms
+
+### System: i7-8750H; 12-cores @ 2.20GHz
+
+**Enabled: Detection, Landmarks, Age, Gender, Emotion, Descriptor**
+
+- FaceAPI: Processed: 69 images in total: 13176 ms working: 12613 ms average: 182 ms
+- Human: Processed: 69 images in total: 10038 ms working: 9315 ms average: 135 ms
+
+<br>
+
+For image processing, Human is ~2x faster than FaceAPI with much higher level of details  
+
+For video processing, difference is even higher  
+since `FaceAPI` processes each frame independently while `Human` has aggressive caching between frames

Home.md

@@ -46,6 +46,7 @@ Check out [**Live Demo**](https://vladmandic.github.io/human/demo/index.html) fo
 - [**Face Recognition & Face Description**](https://github.com/vladmandic/human/wiki/Embedding)
 - [**Gesture Recognition**](https://github.com/vladmandic/human/wiki/Gesture)
 - [**Common Issues**](https://github.com/vladmandic/human/wiki/Issues)
+- [**Background and Benchmarks**](https://github.com/vladmandic/human/wiki/Background)
 
 ## Additional notes