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

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 🙂

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])

Reference: InsightFace