Description:
facelib 1.2.1
facelib
Face recognition python library(tensorflow, opencv).
Usage (console)
try facelib --help to discover more
Train
foo@bar:~$ python3 -m facelib train train_images/ lotr
Current pipeline: ssd_int8_cpu, mobilenetv2_fp32_cpu, densenet_fp32_cpu
Classifier named `lotr` succesfully trained and saved.
Folder structure:
train_images/
├───elijah_wood/
├───├──0.jpg
├───├──1.jpg
├───liv_tyler/
├───├──0.jpg
├───├──1.jpg
...
Image Name
Image
train_images/ian_mckellen/0.jpg
train_images/seanastin/0.jpg
Predict
foo@bar:~$ python3 -m facelib predict test_images/ -clf lotr -c -p
Current pipeline: ssd_int8_cpu, mobilenetv2_fp32_cpu, densenet_fp32_cpu
1.jpg
├───10 faces detected
├───['billy_boyd', 'sean_astin', 'viggo_mortensen', 'elijah_wood', 'liv_tyler', 'dominic_monaghan', 'sean_bean', 'ian_mckellen', 'peter_jackson', 'orlando_bloom']
2.jpg
├───5 faces detected
├───['dominic_monaghan', 'billy_boyd', 'elijah_wood', 'sean_astin', 'peter_jackson']
3.jpg
├───6 faces detected
├───['orlando_bloom', 'dominic_monaghan', 'john_rhys_davies', 'sean_astin', 'elijah_wood', 'billy_boyd']
0.jpeg
├───5 faces detected
├───['dominic_monaghan', 'orlando_bloom', 'elijah_wood', 'liv_tyler', 'billy_boyd']
Folder structure:
test_images/
├──0.jpeg
├──1.jpg
├──2.jpg
├──3.jpg
Generated folders/files:
test_images_facelib_cropped/
├───elijah_wood/
├───├──2_2.jpg
├───├──3_1.jpg
├───├──4_3.jpg
├───liv_tyler/
├───├──3_0.jpg
├───├──4_1.jpg
...
Image Name
Image
test_images_facelib_cropped/billy_boyd/0_1.jpg
test_images_facelib_cropped/liv_tyler/4_1.jpg
test_images_facelib_cropped/elijah_wood/3_1.jpg
test_images_facelib_plotted/1.jpg
Usage (python)
from facelib import facerec
import cv2
# You can use face_detector, landmark_detector or feature_extractor individually using .predict method. e.g.(bboxes = facedetector.predict(img))
face_detector = facerec.SSDFaceDetector()
landmark_detector = facerec.LandmarkDetector()
feature_extractor = facerec.FeatureExtractor()
pipeline = facerec.Pipeline(face_detector, landmark_detector, feature_extractor)
path_img = './path_to_some_image.jpg'
img = cv2.imread(path_img)
img = img[...,::-1] # cv2 returns bgr format but every method inside this package takes rgb format
bboxes, landmarks, features = pipeline.predict(img)
# Note that values returned (bboxes and landmarks) are in fraction.[0,1]
Installation
Pip installation
pip3 install facelib
TFLite runtime installation
To use facelib.facerec package use the following bash command to install tflite-runtime pip package.
python3 -m facelib --install-tflite
or you can install from tensorflow.org
Dev package
Tensorflow is required for facelib.dev package. If you wish you can download facelib with tensorflow using the following command.
pip3 install facelib[dev]
Info
Dataset
Feature extraction models are trained using insightfaces MS1M-Arcface.
Landmark Detection models are trained using VggFace2.
Contents
Image Augmentation
Random augmentation for landmark detection
Layers
DisturbLabel
Face Alignment
Insightface
GoldenRatio
Custom Implementations
TFRecords
Widerface to TFRecords converter
VggFace2 to TFRecords converter
COFW to TFRecords converter
Loss Functions
Feature Extraction
ArcFace
CombinedMargin
SphereFace(A-Softmax)
Center
CosFace
Landmark Detection
EuclideanDistance(with different norms)
Pretrained Models
Face Detection
SSD
MTCNN
Face Feature Extraction
MobileFaceNet
SqueezeNet
MobileNet
MobileNetV2
DenseNet
NasNetMobile
Scripts
Feature extraction model training
Landmark detection model training
Chokepoint test on pipeline
Facial Landmark Detection
SqueezeNet
MobileNet
MobileNetV2
DenseNet
References
WiderFace
Yang, Shuo, Ping Luo, Chen Change Loy, and Xiaoou Tang. “WIDER FACE: A Face Detection Benchmark.” ArXiv:1511.06523 [Cs], November 20, 2015. https://arxiv.org/abs/1511.06523
ArcFace
Deng, Jiankang, Jia Guo, Niannan Xue, and Stefanos Zafeiriou. “ArcFace: Additive Angular Margin Loss for Deep Face Recognition.” ArXiv:1801.07698 [Cs], January 23, 2018. https://arxiv.org/abs/1801.07698
MobileFaceNet
Chen, Sheng, Yang Liu, Xiang Gao, and Zhen Han. “MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices.” CoRR abs/1804.07573 (2018). http://arxiv.org/abs/1804.07573
VggFace2
Cao, Qiong, Li Shen, Weidi Xie, Omkar M. Parkhi, and Andrew Zisserman. “VGGFace2: A Dataset for Recognising Faces across Pose and Age.” ArXiv:1710.08092 [Cs], October 23, 2017. http://arxiv.org/abs/1710.08092
DenseNet
G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks,” arXiv:1608.06993 [cs], Jan. 2018. http://arxiv.org/abs/1608.06993
GoldenRatio (face alignment)
M. Hassaballah, K. Murakami, and S. Ido, “Face detection evaluation: a new approach based on the golden ratio,” SIViP, vol. 7, no. 2, pp. 307–316, Mar. 2013. http://link.springer.com/10.1007/s11760-011-0239-3
SqueezeNet
F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer, “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size,” arXiv:1602.07360 [cs], Feb. 2016. http://arxiv.org/abs/1602.07360
MobileNet
A. G. Howard et al., “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” arXiv:1704.04861 [cs], Apr. 2017. http://arxiv.org/abs/1704.04861
MobileNetV2
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” arXiv:1801.04381 [cs], Jan. 2018. http://arxiv.org/abs/1801.04381
CosFace
H. Wang et al., “CosFace: Large Margin Cosine Loss for Deep Face Recognition,” arXiv:1801.09414 [cs], Jan. 2018. http://arxiv.org/abs/1801.09414
SphereFace
W. Liu, Y. Wen, Z. Yu, M. Li, B. Raj, and L. Song, “SphereFace: Deep Hypersphere Embedding for Face Recognition,” arXiv:1704.08063 [cs], Apr. 2017. http://arxiv.org/abs/1704.08063
Bottleneck Layer
K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” arXiv:1512.03385 [cs], Dec. 2015. http://arxiv.org/abs/1512.03385
MS-Celeb-1M
Y. Guo, L. Zhang, Y. Hu, X. He, and J. Gao, “MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition,” arXiv:1607.08221 [cs], Jul. 2016. http://arxiv.org/abs/1607.08221
DisturbLabel
arXiv:1605.00055 [cs.CV]
Single Shot Detector
[1]W. Liu et al., “SSD: Single Shot MultiBox Detector,” arXiv:1512.02325 [cs], Dec. 2016. https://arxiv.org/abs/1512.02325
Links
Insightface
https://github.com/deepinsight/insightface
Tensorflow
https://github.com/tensorflow/tensorflow
Tensorflow-Addons
https://github.com/tensorflow/addons
Insightface-DatasetZoo
https://github.com/deepinsight/insightface/wiki/Dataset-Zoo
Tensorflow-ModelZoo
https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
Cascade Data
https://github.com/opencv/opencv/tree/master/data
TFLite Python
https://www.tensorflow.org/lite/guide/python
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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