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FaceLib: Face Analysis

Used for face detection, facial expression, AgeGender estimation and recognition with PyTorch.

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

How to use:

Check this example_notebook or take a look at the following sections

1. Face Detection: RetinaFace

You can use these backbone networks: Resnet50, mobilenet. Default model is mobilenet and it will be automatically downloaded.

  • The following example illustrates the ease of use of this package on your webcam:
     from facelib import WebcamFaceDetector
   detector = WebcamFaceDetector()
   detector.run()
  • Low-level access to bounding boxes and face landmarks
   from facelib import FaceDetector
   detector = FaceDetector()
   boxes, scores, landmarks = detector.detect_faces(image)

2. Face Alignment: Using face landmarkd

For face aligment always use the detect_align function it gives you better performance.

  • Face detection and aligment using the detect_align function.
 from facelib import FaceDetector
 detector = FaceDetector()
 faces, boxes, scores, landmarks = detector.detect_align(image)
Original Aligned & Resized Original Aligned & Resized
image image image image

3. Age & Gender Estimation:

ShufflenetFull is the default model, and it will be automatically downloaded.

  • Age and gender estimation live on your webcam (or any camera)
from facelib import WebcamAgeGenderEstimator
estimator = WebcamAgeGenderEstimator()
estimator.run()
  • Low-lvel access to ages and genders
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)

4. Facial Expression Recognition:

The default model is densnet121 and it will be automatically downloaded. Note that face size must be (224, 224).

  • Emotion detector live on your webcam
from facelib import WebcamEmotionDetector
detector = WebcamEmotionDetector()
detector.run()
  • Emotions as an array with their probabilities
from facelib import FaceDetector, EmotionDetector
face_detector = FaceDetector(face_size=(224, 224))
emotion_detector = EmotionDetector()

faces, boxes, scores, landmarks = face_detector.detect_align(image)
emotions, probab = emotion_detector.detect_emotion(faces)
  • on my Webcam 🙂 Alt Text

5. Face Recognition: InsightFace

  • This module is a pytorch 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

Save the images of the faces you want to detect 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 2 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')

Recognizer

The default model is mobilenet and it will be automatically downloaded

  • Face Recognition live on your webcam
from facelib import WebcamVerify
verifier = WebcamVerify(update=True)
verifier.run()
  • Low-level access to your images
import cv2
from facelib import FaceRecognizer, FaceDetector
from facelib import update_facebank, load_facebank, special_draw, get_config

conf = get_config()
# conf.use_mobilenet=False # if you want to use the bigger model
detector = FaceDetector(device=conf.device)
face_rec = FaceRecognizer(conf)

# set True when you add someone new to the facebank
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(faces, targets)
print(names[results.cpu()])
for idx, bbox in enumerate(boxes):
    special_draw(image, bbox, landmarks[idx], names[results[idx]+1], score[idx])

image

Reference: InsightFace