Readme
Face recognition python library(tensorflow, opencv).
try facelib --help to discover more
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.
| Image Name | Image |
|---|---|
| train_images/ian_mckellen/0.jpg | <img src=https://github.com/kutayyildiz/facelib/raw/master/facelib/_demo/lotr/train_images/ian_mckellen/0.jpg width=200 height=100> |
| train_images/seanastin/0.jpg |  |
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 |  |
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]
pip3 install facelib
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
Tensorflow is required for facelib.dev package. If you wish you can download facelib with tensorflow using the following command.
pip3 install facelib[dev]
Feature extraction models are trained using insightfaces MS1M-Arcface.
Landmark Detection models are trained using VggFace2.
| 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 |
| 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 |
FAQs
Face Recognition (train/test/deploy)(tensorflow/tflite/keras/edgetpu)
We found that facelib demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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