Face Detection & Age Gender & Expression & Recognition

Overview

FaceLib:

  • 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()

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

Style easy medium hard
Pytorch (same parameter with Mxnet) 88.67% 87.09% 80.99%
Pytorch (original image scale) 90.70% 88.16% 73.82%
Mxnet(original image scale) 89.58% 87.11% 69.12%

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
image image image image

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 🙂

Alt Text

5. Face Recognition: InsightFace

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

Pretrained Models & Performance

  • IR-SE50
LFW(%) CFP-FF(%) CFP-FP(%) AgeDB-30(%) calfw(%) cplfw(%) vgg2_fp(%)
0.9952 0.9962 0.9504 0.9622 0.9557 0.9107 0.9386
  • Mobilefacenet
LFW(%) CFP-FF(%) CFP-FP(%) AgeDB-30(%) calfw(%) cplfw(%) vgg2_fp(%)
0.9918 0.9891 0.8986 0.9347 0.9402 0.866 0.9100

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:

image

Reference:

Owner
Sajjad Ayobi
Data Science Lover, a Little Geek
Sajjad Ayobi
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