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https://github.com/sajjjadayobi/FaceLib
Face Analysis: Detection, Age Gender Estimation & Recognition
https://github.com/sajjjadayobi/FaceLib
age-gender deep-learning face face-detection face-recognition facial-expression-recognition gender-estimation pytorch recognition retinaface
Last synced: 3 months ago
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Face Analysis: Detection, Age Gender Estimation & Recognition
- Host: GitHub
- URL: https://github.com/sajjjadayobi/FaceLib
- Owner: sajjjadayobi
- License: mit
- Created: 2020-02-09T20:43:46.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2024-06-12T17:40:34.000Z (5 months ago)
- Last Synced: 2024-06-28T10:33:02.220Z (5 months ago)
- Topics: age-gender, deep-learning, face, face-detection, face-recognition, facial-expression-recognition, gender-estimation, pytorch, recognition, retinaface
- Language: Python
- Homepage:
- Size: 10.3 MB
- Stars: 283
- Watchers: 5
- Forks: 51
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# [FaceLib](https://github.com/sajjjadayobi/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](https://github.com/sajjjadayobi/FaceLib/blob/master/example_notebook.ipynb) 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:
```python
from facelib import WebcamFaceDetector
detector = WebcamFaceDetector()
detector.run()
```
- Low-level access to bounding boxes and face landmarks
```python
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.
```python
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)
```python
from facelib import WebcamAgeGenderEstimator
estimator = WebcamAgeGenderEstimator()
estimator.run()
```
- Low-lvel access to ages and genders
```python
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
```python
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.
```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.
```python
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
```python
from facelib import WebcamVerify
verifier = WebcamVerify(update=True)
verifier.run()
```
- Low-level access to your images
```python
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](https://github.com/TreB1eN/InsightFace_Pytorch)