**JavaScript API for face detection and face recognition in the browser implemented on top of the tensorflow.js core API ([tensorflow/tfjs-core](https://github.com/tensorflow/tfjs-core))**
**JavaScript face recognition API for the browser and nodejs implemented on top of tensorflow.js core ([tensorflow/tfjs-core](https://github.com/tensorflow/tfjs-core))**
**[Check out the live demos!](https://justadudewhohacks.github.io/face-api.js/)**
<aname="tutorials"></a>
## Tutorials
## Tutorials
Check out my face-api.js tutorials:
***[face-api.js — JavaScript API for Face Recognition in the Browser with tensorflow.js](https://itnext.io/face-api-js-javascript-api-for-face-recognition-in-the-browser-with-tensorflow-js-bcc2a6c4cf07)**
***[face-api.js — JavaScript API for Face Recognition in the Browser with tensorflow.js](https://itnext.io/face-api-js-javascript-api-for-face-recognition-in-the-browser-with-tensorflow-js-bcc2a6c4cf07)**
***[Realtime JavaScript Face Tracking and Face Recognition using face-api.js’ MTCNN Face Detector](https://itnext.io/realtime-javascript-face-tracking-and-face-recognition-using-face-api-js-mtcnn-face-detector-d924dd8b5740)**
***[Realtime JavaScript Face Tracking and Face Recognition using face-api.js’ MTCNN Face Detector](https://itnext.io/realtime-javascript-face-tracking-and-face-recognition-using-face-api-js-mtcnn-face-detector-d924dd8b5740)**
<aname="examples"></a>
## Table of Contents
***[Features](#features)**
***[Running the Examples](#running-the-examples)**
***[face-api.js for the Browser](#face-api.js-for-the-browser)**
***[face-api.js for Nodejs](#face-api.js-for-nodejs)**
***[Usage](#getting-started)**
***[Loading the Models](#getting-started-loading-models)**
For face detection, this project implements a SSD (Single Shot Multibox Detector) based on MobileNetV1. The neural net will compute the locations of each face in an image and will return the bounding boxes together with it's probability for each face. This face detector is aiming towards obtaining high accuracy in detecting face bounding boxes instead of low inference time. The size of the quantized model is about 5.4 MB (**ssd_mobilenetv1_model**).
The face detection model has been trained on the [WIDERFACE dataset](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/) and the weights are provided by [yeephycho](https://github.com/yeephycho) in [this](https://github.com/yeephycho/tensorflow-face-detection) repo.
### Tiny Face Detector
The Tiny Face Detector is a very performant, realtime face detector, which is much faster, smaller and less resource consuming compared to the SSD Mobilenet V1 face detector, in return it performs slightly less well on detecting small faces. This model is extremely mobile and web friendly, thus it should be your GO-TO face detector on mobile devices and resource limited clients. The size of the quantized model is only 190 KB (**tiny_face_detector_model**).
The face detector has been trained on a custom dataset of ~14K images labeled with bounding boxes. Furthermore the model has been trained to predict bounding boxes, which entirely cover facial feature points, thus it in general produces better results in combination with subsequent face landmark detection than SSD Mobilenet V1.
This model is basically an even tinier version of Tiny Yolo V2, replacing the regular convolutions of Yolo with depthwise separable convolutions. Yolo is fully convolutional, thus can easily adapt to different input image sizes to trade off accuracy for performance (inference time).
### MTCNN
**Note, this model is mostly kept in this repo for experimental reasons. In general the other face detectors should perform better, but of course you are free to play around with MTCNN.**
MTCNN (Multi-task Cascaded Convolutional Neural Networks) represents an alternative face detector to SSD Mobilenet v1 and Tiny Yolo v2, which offers much more room for configuration. By tuning the input parameters, MTCNN should be able to detect a wide range of face bounding box sizes. MTCNN is a 3 stage cascaded CNN, which simultaneously returns 5 face landmark points along with the bounding boxes and scores for each face. Additionally the model size is only 2MB.
MTCNN has been presented in the paper [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://kpzhang93.github.io/MTCNN_face_detection_alignment/paper/spl.pdf) by Zhang et al. and the model weights are provided in the official [repo](https://github.com/kpzhang93/MTCNN_face_detection_alignment) of the MTCNN implementation.
<aname="models-face-landmark-detection"></a>
## 68 Point Face Landmark Detection Models
This package implements a very lightweight and fast, yet accurate 68 point face landmark detector. The default model has a size of only 350kb (**face_landmark_68_model**) and the tiny model is only 80kb (**face_landmark_68_tiny_model**). Both models employ the ideas of depthwise separable convolutions as well as densely connected blocks. The models have been trained on a dataset of ~35k face images labeled with 68 face landmark points.
<aname="models-face-recognition"></a>
## Face Recognition Model
For face recognition, a ResNet-34 like architecture is implemented to compute a face descriptor (a feature vector with 128 values) from any given face image, which is used to describe the characteristics of a persons face. The model is **not** limited to the set of faces used for training, meaning you can use it for face recognition of any person, for example yourself. You can determine the similarity of two arbitrary faces by comparing their face descriptors, for example by computing the euclidean distance or using any other classifier of your choice.
The neural net is equivalent to the **FaceRecognizerNet** used in [face-recognition.js](https://github.com/justadudewhohacks/face-recognition.js) and the net used in the [dlib](https://github.com/davisking/dlib/blob/master/examples/dnn_face_recognition_ex.cpp) face recognition example. The weights have been trained by [davisking](https://github.com/davisking) and the model achieves a prediction accuracy of 99.38% on the LFW (Labeled Faces in the Wild) benchmark for face recognition.
The size of the quantized model is roughly 6.2 MB (**face_recognition_model**).
<aname="models-face-expression-recognition"></a>
## Face Expression Recognition Model
The face expression recognition model is lightweight, fast and provides reasonable accuracy. The model has a size of roughly 310kb and it employs depthwise separable convolutions and densely connected blocks. It has been trained on a variety of images from publicly available datasets as well as images scraped from the web. Note, that wearing glasses might decrease the accuracy of the prediction results.
<aname="getting-started"></a>
# Getting Started
<aname="getting-started-browser"></a>
## face-api.js for the Browser
# face-api.js for the Browser
Simply include the latest script from [dist/face-api.js](https://github.com/justadudewhohacks/face-api.js/tree/master/dist).
Simply include the latest script from [dist/face-api.js](https://github.com/justadudewhohacks/face-api.js/tree/master/dist).
...
@@ -188,9 +105,9 @@ Or install it via npm:
...
@@ -188,9 +105,9 @@ Or install it via npm:
npm i face-api.js
npm i face-api.js
```
```
<aname="getting-started-nodejs"></a>
<aname="face-api.js-for-nodejs"></a>
## face-api.js for Nodejs
# face-api.js for Nodejs
We can use the equivalent API in a nodejs environment by polyfilling some browser specifics, such as HTMLImageElement, HTMLCanvasElement and ImageData. The easiest way to do so is by installing the node-canvas package.
We can use the equivalent API in a nodejs environment by polyfilling some browser specifics, such as HTMLImageElement, HTMLCanvasElement and ImageData. The easiest way to do so is by installing the node-canvas package.
To load a model, you have provide the corresponding manifest.json file as well as the model weight files (shards) as assets. Simply copy them to your public or assets folder. The manifest.json and shard files of a model have to be located in the same directory / accessible under the same route.
All global neural network instances are exported via faceapi.nets:
All global neural network instances are exported via faceapi.nets:
``` javascript
``` javascript
console.log(faceapi.nets)
console.log(faceapi.nets)
// ageGenderNet
// faceExpressionNet
// faceLandmark68Net
// faceLandmark68TinyNet
// faceRecognitionNet
// ssdMobilenetv1
// tinyFaceDetector
// mtcnn
// tinyYolov2
```
```
The following is equivalent to `await faceapi.loadSsdMobilenetv1Model('/models')`:
To load a model, you have provide the corresponding manifest.json file as well as the model weight files (shards) as assets. Simply copy them to your public or assets folder. The manifest.json and shard files of a model have to be located in the same directory / accessible under the same route.
You can tune the options of each face detector as shown [here](#usage-face-detection-options).
You can tune the options of each face detector as shown [here](#getting-started-face-detection-options).
### Detecting 68 Face Landmark Points
### Detecting 68 Face Landmark Points
**After face detection, we can furthermore predict the facial landmarks for each detected face as follows:**
**After face detection, we can furthermore predict the facial landmarks for each detected face as follows:**
Detect all faces in an image + computes 68 Point Face Landmarks for each detected face. Returns **Array<[WithFaceLandmarks<WithFaceDetection<{}>>](#usage-utility-classes)>**:
Detect all faces in an image + computes 68 Point Face Landmarks for each detected face. Returns **Array<[WithFaceLandmarks<WithFaceDetection<{}>>](#getting-started-utility-classes)>**:
Detect the face with the highest confidence score in an image + computes 68 Point Face Landmarks for that face. Returns **[WithFaceLandmarks<WithFaceDetection<{}>>](#usage-utility-classes) | undefined**:
Detect the face with the highest confidence score in an image + computes 68 Point Face Landmarks for that face. Returns **[WithFaceLandmarks<WithFaceDetection<{}>>](#getting-started-utility-classes) | undefined**:
**After face detection and facial landmark prediction the face descriptors for each face can be computed as follows:**
**After face detection and facial landmark prediction the face descriptors for each face can be computed as follows:**
Detect all faces in an image + computes 68 Point Face Landmarks for each detected face. Returns **Array<[WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>](#usage-utility-classes)>**:
Detect all faces in an image + computes 68 Point Face Landmarks for each detected face. Returns **Array<[WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>](#getting-started-utility-classes)>**:
Detect the face with the highest confidence score in an image + computes 68 Point Face Landmarks and face descriptor for that face. Returns **[WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>](#usage-utility-classes) | undefined**:
Detect the face with the highest confidence score in an image + computes 68 Point Face Landmarks and face descriptor for that face. Returns **[WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>](#getting-started-utility-classes) | undefined**:
@@ -370,18 +290,62 @@ const result = await faceapi.detectSingleFace(input).withFaceLandmarks().withFac
...
@@ -370,18 +290,62 @@ const result = await faceapi.detectSingleFace(input).withFaceLandmarks().withFac
**Face expression recognition can be performed for detected faces as follows:**
**Face expression recognition can be performed for detected faces as follows:**
Detect all faces in an image + recognize face expressions. Returns **Array<[WithFaceExpressions<WithFaceDetection<{}>>](#usage-utility-classes)>**:
Detect all faces in an image + recognize face expressions. Returns **Array<[WithFaceExpressions<WithFaceLandmarks<WithFaceDetection<{}>>>](#getting-started-utility-classes)>**:
Detect the face with the highest confidence score in an image + recognize the face expression for that face. Returns **[WithFaceExpressions<WithFaceLandmarks<WithFaceDetection<{}>>>](#getting-started-utility-classes) | undefined**:
**You can also skip .withFaceLandmarks(), which will skip the face alignment step (less stable accuracy):**
Detect all faces without face alignment + recognize face expressions. Returns **Array<[WithFaceExpressions<WithFaceDetection<{}>>](#getting-started-utility-classes)>**:
Detect the face with the highest confidence score in an image + recognize the face expression for that face. Returns **[WithFaceExpressions<WithFaceDetection<{}>>](#usage-utility-classes) | undefined**:
Detect the face with the highest confidence score without face alignment + recognize the face expression for that face. Returns **[WithFaceExpressions<WithFaceDetection<{}>>](#getting-started-utility-classes) | undefined**:
**Age estimation and gender recognition from detected faces can be done as follows:**
Detect all faces in an image + estimate age and recognize gender. Returns **Array<[WithAge<WithGender<WithFaceLandmarks<WithFaceDetection<{}>>>>](#getting-started-utility-classes)>**:
Detect the face with the highest confidence score in an image + recognize the face expression for that face. Returns **[WithAge<WithGender<WithFaceLandmarks<WithFaceDetection<{}>>>>](#getting-started-utility-classes) | undefined**:
**You can also skip .withFaceLandmarks(), which will skip the face alignment step (less stable accuracy):**
Detect all faces without face alignment + recognize face expressions. Returns **Array<[WithAge<WithGender<WithFaceDetection<{}>>>](#getting-started-utility-classes)>**:
Detect the face with the highest confidence score without face alignment + recognize the face expression for that face. Returns **[WithAge<WithGender<WithFaceDetection<{}>>>](#getting-started-utility-classes) | undefined**:
For face detection, this project implements a SSD (Single Shot Multibox Detector) based on MobileNetV1. The neural net will compute the locations of each face in an image and will return the bounding boxes together with it's probability for each face. This face detector is aiming towards obtaining high accuracy in detecting face bounding boxes instead of low inference time. The size of the quantized model is about 5.4 MB (**ssd_mobilenetv1_model**).
The face detection model has been trained on the [WIDERFACE dataset](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/) and the weights are provided by [yeephycho](https://github.com/yeephycho) in [this](https://github.com/yeephycho/tensorflow-face-detection) repo.
### Tiny Face Detector
The Tiny Face Detector is a very performant, realtime face detector, which is much faster, smaller and less resource consuming compared to the SSD Mobilenet V1 face detector, in return it performs slightly less well on detecting small faces. This model is extremely mobile and web friendly, thus it should be your GO-TO face detector on mobile devices and resource limited clients. The size of the quantized model is only 190 KB (**tiny_face_detector_model**).
The face detector has been trained on a custom dataset of ~14K images labeled with bounding boxes. Furthermore the model has been trained to predict bounding boxes, which entirely cover facial feature points, thus it in general produces better results in combination with subsequent face landmark detection than SSD Mobilenet V1.
This model is basically an even tinier version of Tiny Yolo V2, replacing the regular convolutions of Yolo with depthwise separable convolutions. Yolo is fully convolutional, thus can easily adapt to different input image sizes to trade off accuracy for performance (inference time).
### MTCNN
**Note, this model is mostly kept in this repo for experimental reasons. In general the other face detectors should perform better, but of course you are free to play around with MTCNN.**
MTCNN (Multi-task Cascaded Convolutional Neural Networks) represents an alternative face detector to SSD Mobilenet v1 and Tiny Yolo v2, which offers much more room for configuration. By tuning the input parameters, MTCNN should be able to detect a wide range of face bounding box sizes. MTCNN is a 3 stage cascaded CNN, which simultaneously returns 5 face landmark points along with the bounding boxes and scores for each face. Additionally the model size is only 2MB.
MTCNN has been presented in the paper [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://kpzhang93.github.io/MTCNN_face_detection_alignment/paper/spl.pdf) by Zhang et al. and the model weights are provided in the official [repo](https://github.com/kpzhang93/MTCNN_face_detection_alignment) of the MTCNN implementation.
<aname="models-face-landmark-detection"></a>
## 68 Point Face Landmark Detection Models
This package implements a very lightweight and fast, yet accurate 68 point face landmark detector. The default model has a size of only 350kb (**face_landmark_68_model**) and the tiny model is only 80kb (**face_landmark_68_tiny_model**). Both models employ the ideas of depthwise separable convolutions as well as densely connected blocks. The models have been trained on a dataset of ~35k face images labeled with 68 face landmark points.
<aname="models-face-recognition"></a>
## Face Recognition Model
For face recognition, a ResNet-34 like architecture is implemented to compute a face descriptor (a feature vector with 128 values) from any given face image, which is used to describe the characteristics of a persons face. The model is **not** limited to the set of faces used for training, meaning you can use it for face recognition of any person, for example yourself. You can determine the similarity of two arbitrary faces by comparing their face descriptors, for example by computing the euclidean distance or using any other classifier of your choice.
The neural net is equivalent to the **FaceRecognizerNet** used in [face-recognition.js](https://github.com/justadudewhohacks/face-recognition.js) and the net used in the [dlib](https://github.com/davisking/dlib/blob/master/examples/dnn_face_recognition_ex.cpp) face recognition example. The weights have been trained by [davisking](https://github.com/davisking) and the model achieves a prediction accuracy of 99.38% on the LFW (Labeled Faces in the Wild) benchmark for face recognition.
The size of the quantized model is roughly 6.2 MB (**face_recognition_model**).
<aname="models-face-expression-recognition"></a>
## Face Expression Recognition Model
The face expression recognition model is lightweight, fast and provides reasonable accuracy. The model has a size of roughly 310kb and it employs depthwise separable convolutions and densely connected blocks. It has been trained on a variety of images from publicly available datasets as well as images scraped from the web. Note, that wearing glasses might decrease the accuracy of the prediction results.
