Background

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.

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

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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!

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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

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