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
```python
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](https://github.com/sajjjadayobi/FaceLib/blob/master/facelib/imgs/emotion.gif)

## 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.
```python
 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](https://github.com/sajjjadayobi/FaceLib/blob/master/facelib/imgs/face_rec.jpg)

Reference: [InsightFace](https://github.com/TreB1eN/InsightFace_Pytorch)

sajjjadayobi / FaceLib

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