Face Detection & Age Gender & Expression & Recognition with PyTorch

FaceLib

Face Detection & Age Gender & Expression & Recognition

• use for Detection, Facial Expression, Age & Gender Estimation and Recognition with PyTorch
• this repository works with CPU and GPU(Cuda)

Installation

• Clone and install with this command:

  • with pip and automatic installs everything all you need

    • pip install git+https://github.com/sajjjadayobi/FaceLib.git

  • or with cloning the repo and install required packages

    • git clone https://github.com/sajjjadayobi/FaceLib.git
• you can see the required packages in requirements.txt

How to use:

• the simplest way is at example_notebook.ipynb
• for low-level usage check out the following sections
• if you have an NVIDIA GPU don't change the device param if not use cpu

1. Face Detection: RetinaFace

• you can use these backbone networks: Resnet50, mobilenet
  • default weights and model is mobilenet and it will be automatically download
• for more details, you can see the documentation
• The following example illustrates the ease of use of this package:

 from facelib import FaceDetector
 detector = FaceDetector()
 boxes, scores, landmarks = detector.detect_faces(image)

• FaceDetection live on your webcam

   from facelib import WebcamFaceDetector
   detector = WebcamFaceDetector()
   detector.run()

• you can check or change it Face Detector

WiderFace Validation Performance on a single scale When using Mobilenet for backbone

2. Face Alignment: Similar Transformation

• always use detect_align it gives you better performance
• you can use this module like this:
  • detect_align instead of detect_faces

 from facelib import FaceDetector
 detector = FaceDetector()
 faces, boxes, scores, landmarks = detector.detect_align(image)

• for more details read detect_image function documentation
• let's see a few examples

Original	Aligned & Resized	Original	Aligned & Resized

3. Age & Gender Estimation:

• I used UTKFace DataSet for Age & Gender Estimation
  • default weights and model is ShufflenetFull and it will be automatically download
• you can use this module like this:

   from facelib import FaceDetector, AgeGenderEstimator

   face_detector = FaceDetector()
   age_gender_detector = AgeGenderEstimator()

   faces, boxes, scores, landmarks = face_detector.detect_align(image)
   genders, ages = age_gender_detector.detect(faces)
   print(genders, ages)

• AgeGenderEstimation live on your webcam

   from facelib import WebcamAgeGenderEstimator
   estimator = WebcamAgeGenderEstimator()
   estimator.run()

4. Facial Expression Recognition:

• Facial Expression Recognition using Residual Masking Network
  • default weights and model is densnet121 and it will be automatically download
• face size must be (224, 224), you can fix it in FaceDetector init function with face_size=(224, 224)

  from facelib import FaceDetector, EmotionDetector
 
  face_detector = FaceDetector(face_size=(224, 224))
  emotion_detector = EmotionDetector()

  faces, boxes, scores, landmarks = face_detector.detect_align(image)
  list_of_emotions, probab = emotion_detector.detect_emotion(faces)
  print(list_of_emotions)

• EmotionDetector live on your webcam

   from facelib import WebcamEmotionDetector
   detector = WebcamEmotionDetector()
   detector.run()

• on my Webcam ?

5. Face Recognition: InsightFace

• This module is a reimplementation of Arcface(paper), or Insightface(Github)

Pretrained Models & Performance

• IR-SE50

• Mobilefacenet

Prepare the Facebank (For testing over camera, video or image)

• the faces images you want to detect it save them in this folder:

  Insightface/models/data/facebank/
            ---> person_1/
                ---> img_1.jpg
                ---> img_2.jpg
            ---> person_2/
                ---> img_1.jpg
                ---> img_2.jpg
  

• you can save a new preson in facebank with 3 ways:

  • use add_from_webcam: it takes 4 images and saves them on facebank

     from facelib import add_from_webcam
     add_from_webcam(person_name='sajjad')
  

  • use add_from_folder: it takes a path with some images from just a person

     from facelib import add_from_folder
     add_from_folder(folder_path='./', person_name='sajjad')
  

  • or add faces manually (just face of a person not image of a person)
    • I don't suggest this

Using

• default weights and model is mobilenet and it will be automatically download

    import cv2
    from facelib import FaceRecognizer, FaceDetector
    from facelib import update_facebank, load_facebank, special_draw, get_config
 
    conf = get_config()
    detector = FaceDetector()
    face_rec = FaceRecognizer(conf)
    face_rec.model.eval()
    
    # set True when you add someone new 
    update_facebank_for_add_new_person = False
    if update_facebank_for_add_new_person:
        targets, names = update_facebank(conf, face_rec.model, detector)
    else:
        targets, names = load_facebank(conf)

    image = cv2.imread(your_path)
    faces, boxes, scores, landmarks = detector.detect_align(image)
    results, score = face_rec.infer(conf, faces, targets)
    print(names[results.cpu()])
    for idx, bbox in enumerate(boxes):
        special_draw(image, bbox, landmarks[idx], names[results[idx]+1], score[idx])

• Face Recognition live on your webcam

   from facelib import WebcamVerify
   verifier = WebcamVerify(update=True)
   verifier.run()

• example of run this code:

GitHub

https://github.com/sajjjadayobi/FaceLib