利用Keras进行人脸分析的深度学习框架集合

利用Keras进行人脸分析的深度学习框架集合

face-toolbox-keras

A collection of deep learning frameworks ported to Keras for face detection, face segmentation, face parsing, iris detection, and face verification.

Descriptions

This repository contains deep learning frameworks that we collected and ported to Keras. We wrapped those models into separate modules that aim to provide their functionality to users within 3 lines of code.

Face detection:S3FD model ported from 1adrianb/face-alignment.MTCNN model ported from davidsandberg/facenet. Face landmarks detection:2DFAN-4, 2DFAN-2, and 2DFAN-1 models ported from 1adrianb/face-alignment. Face parsing:BiSeNet model ported from zllrunning/face-parsing.PyTorch. Eye region landmarks detection:ELG model is ported from swook/GazeML. Face verification:InceptionResNetV1 model (model name: 20180402-114759) ported from davidsandberg/facenet.LResNet100E-IR model ported from deepinsight/insightface.IR50 model ported from ZhaoJ9014/face.evoLVe.PyTorch. Gender and age estimation:MobileNet model ported from deepinsight/insightface.

*Each module follows the license of their source repo. Notice that some models were trained on dataset with non-commercial license.

Usage

This colab demo requires a GPU instance. It demonstrates all face analysis functionalities above.

1. Face detection

models.detector.face_detector.FaceAlignmentDetector(fd_weights_path=..., lmd_weights_path=..., fd_type="s3fd")

Arguments

fd_weights_path: A string. Path to weights file of the face detector model.lmd_weights_path: A string. Path to weights file of the landmarks detector model.fd_type: A string. Face detector backbone model of either s3fd or mtcnn.

Example

from models.detector import face_detectorim = cv2.imread(PATH_TO_IMAGE)[..., ::-1]fd = face_detector.FaceAlignmentDetector()bboxes = fd.detect_face(im, with_landmarks=False)

2. Face landmarks detection

The default model is 2DFAN-4. Lite models of 2DFAN-1 and 2DFAN-2 are also provided.

Example

from models.detector import face_detectorim = cv2.imread(PATH_TO_IMAGE)[..., ::-1]fd = face_detector.FaceAlignmentDetector()bboxes, landmarks = fd.detect_face(im, with_landmarks=True)

3. Face parsing

models.parser.face_parser.FaceParser(path_bisenet_weights=...)

Arguments

path_bisenet_weights: A string. Path to weights file of the model.

Example

from models.parser import face_parserim = cv2.imread(PATH_TO_IMAGE)[..., ::-1]fp = face_parser.FaceParser()# fp.set_detector(fd) # fd = face_detector.FaceAlignmentDetector()parsing_map = fp.parse_face(im, bounding_box=None, with_detection=False)

4. Eye region landmarks detection

models.detector.iris_detector.IrisDetector()

Faster face detection using MTCNN can be found in this repo.

Example

from models.detector import iris_detectorim = cv2.imread(PATH_TO_IMAGE)[..., ::-1]idet = iris_detector.IrisDetector()idet.set_detector(fd) # fd = face_detector.FaceAlignmentDetector()eye_landmarks = idet.detect_iris(im)

5. Face verification

models.verifier.face_verifier.FaceVerifier(extractor="facenet", classes=512)

Argument

extractor: A string, one of facenet, insightface, ir50_ms1m, or ir50_asia.classes: An integer. Dimension of output embeddings.

Example

from models.verifier import face_verifierim1 = cv2.imread(PATH_TO_IMAGE1)[..., ::-1]im2 = cv2.imread(PATH_TO_IMAGE2)[..., ::-1]fv = face_verifier.FaceVerifier(extractor="facenet")# fv.set_detector(fd) # fd = face_detector.FaceAlignmentDetector()result, distance = fv.verify(im1, im2, threshold=0.5, with_detection=False, with_alignment=False, return_distance=True)

6. Gender and age estimation

models.estimator.gender_age_estimator.GenderAgeEstimator(model_type="insightface")

Arguments

model_type: A string, only insightface is supported now.

Example

from models.estimator import gender_age_estimatorim = cv2.imread(PATH_TO_IMAGE)[..., ::-1]gae = gender_age_estimator.GenderAgeEstimator()gae.set_detector(fd) # fd = face_detector.FaceAlignmentDetector()gender, age = gae.predict_gender_age(im, with_detection=True)

Ported model weights

insightface model, this file should be put in ./models/verifier/insightface/.IR50-MS-Celeb-1M model, this file should be put in ./models/verifier/face_evoLVe_ir50/.IR50-Asia-face model, this file should be put in ./models/verifier/face_evoLVe_ir50/.

Known issues

It works fine on Colab at this point (2019/06/11) but for certain Keras/TensorFlow version, it throws errors loading 2DFAN-1_keras.h5 or 2DFAN-2_keras.h5.

Requirements

Keras 2.2.4TensorFlow 1.12.0 or 1.13.1

Acknowledgments

We learnt a lot from 1adrianb/face-alignment, zllrunning/face-parsing.PyTorch, swook/GazeML, deepinsight/insightface, davidsandberg/facenet, and ZhaoJ9014/face.evoLVe.PyTorch.

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