FaceX-Zoo: A PyTorch Toolbox for Face Recognition

Due to the remarkable progress in recent years, deep face recognition is in great need of public support for practical model production and further exploration. The demands are in three folds, including 1) modular training scheme, 2) standard and automatic evaluation, and 3) groundwork of deployment. To meet these demands, we present a novel open-source project, named FaceX-Zoo, which is constructed with modular and scalable design, and oriented to the academic and industrial community of face-related analysis. FaceX-Zoo provides 1) the training module with various choices of backbone and supervisory head; 2) the evaluation module that enables standard and automatic test on most popular benchmarks; 3) the module of simple yet fully functional face SDK for the validation and primary application of end-to-end face recognition; 4) the additional module that integrates a group of useful tools. Based on these easy-to-use modules, FaceX-Zoo can help the community to easily build stateof-the-art solutions for deep face recognition and, such like the newly-emerged challenge of masked face recognition caused by the worldwide COVID-19 pandemic. Besides, FaceX-Zoo can be easily upgraded and scaled up along with further exploration in face related fields. The source codes and models have been released and received over 900 stars at https://github.com/JDAI-CV/FaceX-Zoo.

[1]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Mahadev Satyanarayanan,et al.  OpenFace: A general-purpose face recognition library with mobile applications , 2016 .

[3]  Zheng Zhang,et al.  MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems , 2015, ArXiv.

[4]  Jianyuan Guo,et al.  GhostNet: More Features From Cheap Operations , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Lin Ma,et al.  PFLD: A Practical Facial Landmark Detector , 2019, ArXiv.

[6]  Shifeng Zhang,et al.  Mis-classified Vector Guided Softmax Loss for Face Recognition , 2019, AAAI.

[7]  Xi Zhou,et al.  Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network , 2018, ECCV.

[8]  Tao Mei,et al.  Semi-Siamese Training for Shallow Face Learning , 2020, ECCV.

[9]  Dongyoon Han,et al.  Rethinking Channel Dimensions for Efficient Model Design , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  James Philbin,et al.  FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Yang Zhao,et al.  Deep High-Resolution Representation Learning for Visual Recognition , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Hao Shen,et al.  Grand Challenge of 106-Point Facial Landmark Localization , 2019, 2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW).

[13]  Enhua Wu,et al.  Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Shichao Zhao,et al.  MagFace: A Universal Representation for Face Recognition and Quality Assessment , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Yibo Hu,et al.  TF-NAS: Rethinking Three Search Freedoms of Latency-Constrained Differentiable Neural Architecture Search , 2020, ECCV.

[16]  Stefanos Zafeiriou,et al.  RetinaFace: Single-stage Dense Face Localisation in the Wild , 2019, ArXiv.

[17]  Ira Kemelmacher-Shlizerman,et al.  The MegaFace Benchmark: 1 Million Faces for Recognition at Scale , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Stefanos Zafeiriou,et al.  ArcFace: Additive Angular Margin Loss for Deep Face Recognition , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Marwan Mattar,et al.  Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments , 2008 .

[20]  Xiaogang Wang,et al.  AdaCos: Adaptively Scaling Cosine Logits for Effectively Learning Deep Face Representations , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Weihong Deng,et al.  Cross-Pose LFW : A Database for Studying Cross-Pose Face Recognition in Unconstrained Environments , 2018 .

[22]  Xiangyu Zhu,et al.  AdaptiveFace: Adaptive Margin and Sampling for Face Recognition , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Jian Cheng,et al.  Additive Margin Softmax for Face Verification , 2018, IEEE Signal Processing Letters.

[24]  Stefanos Zafeiriou,et al.  AgeDB: The First Manually Collected, In-the-Wild Age Database , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[25]  Hailin Shi,et al.  A New Dataset and Boundary-Attention Semantic Segmentation for Face Parsing , 2020, AAAI.

[26]  Mei Wang,et al.  Racial Faces in the Wild: Reducing Racial Bias by Information Maximization Adaptation Network , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[27]  Ningning Ma,et al.  RepVGG: Making VGG-style ConvNets Great Again , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[28]  Quoc V. Le,et al.  EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.

[29]  Natalia Gimelshein,et al.  PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.

[30]  Shuo Yang,et al.  WIDER FACE: A Face Detection Benchmark , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[31]  Feiyue Huang,et al.  CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Yang Liu,et al.  MobileFaceNets: Efficient CNNs for Accurate Real-time Face Verification on Mobile Devices , 2018, CCBR.

[33]  Zhenan Sun,et al.  Learning an Evolutionary Embedding via Massive Knowledge Distillation , 2020, International Journal of Computer Vision.

[34]  Yichen Wei,et al.  Circle Loss: A Unified Perspective of Pair Similarity Optimization , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[35]  Tieniu Tan,et al.  A Light CNN for Deep Face Representation With Noisy Labels , 2015, IEEE Transactions on Information Forensics and Security.

[36]  Zhen Lei,et al.  NPCFace: A Negative-Positive Cooperation Supervision for Training Large-scale Face Recognition , 2020, arXiv.org.

[37]  Weihong Deng,et al.  Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments , 2017, ArXiv.

[38]  Xiaogang Wang,et al.  Residual Attention Network for Image Classification , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).