DeepID-implementation is an implementation of paper "Deep Learning Face Representation from Predicting 10,000 Classes", which proposes to learn a set of compact, 160-dims high level feature representations through deep learning, referred to as Deep hidden IDentity features (DeepID), for face verification.

More details in DeepID Notebook.

• The classification ability of Multi-scale ConvNets
  • [paper] | [model_62x62_3]
• The effectiveness of the learned hidden representations for face verification
  • [paper] | [model_62x62_3]
• The learned features extract identity information
  • [paper] | [model_62x62_3]
• Various face patches combination contributes to the performance
  • [paper]
• Method comparison
  • [paper] | [model_62x62_3]

with Joint Bayesian, Cosine Similarity, Euclidean Distance method.

[1]. Yi Sun, Xiaogang Wang, Xiaoao Tang, Deep Learning Face Representation from Predicting 10,000 Classes, 2014-06-23

[2]. RiweiChen, Caffe实践-基于Caffe的人脸识别实现, 2015-11-01

[3]. RiweiChen, 深度学习论文笔记-Deep Learning Face Representation from Predicting 10,000 Classes, 2014-06-16

[4]. 張雨石, DeepID人脸识别算法之三代, 2014-12-23

[5]. RiweiChen, RiweiChen/DeepFace, 2016-04-14

[6]. Feng Wang, happynear/FaceVerification, 2016-04-25

[7]. Alfred Xiang Wu, AlfredXiangWu/face_verification_experiment, 2015-12-14