Triplet Similarity Embedding for Face Verification

In this work, we present an unconstrained face verification algorithm and evaluate it on the recently released IJB-A dataset that aims to push the boundaries of face verification methods. The proposed algorithm couples a deep CNN-based approach with a low-dimensional discriminative embedding learnt using triplet similarity constraints in a large margin fashion. Aside from yielding performance improvement, this embedding provides significant advantages in terms of memory and post-processing operations like hashing and visualization. Experiments on the IJB-A dataset show that the proposed algorithm outperforms state of the art methods in verification and identification metrics, while requiring less training time.

[1]  Anil K. Jain,et al.  Face Search at Scale: 80 Million Gallery , 2015, ArXiv.

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

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

[4]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[5]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[6]  Andrew Zisserman,et al.  Fisher Vector Faces in the Wild , 2013, BMVC.

[7]  Anil K. Jain,et al.  Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Andrew Zisserman,et al.  Deep Face Recognition , 2015, BMVC.

[9]  Swami Sankaranarayanan,et al.  Unconstrained face verification using fisher vectors computed from frontalized faces , 2015, 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS).

[10]  Kilian Q. Weinberger,et al.  Distance Metric Learning for Large Margin Nearest Neighbor Classification , 2005, NIPS.

[11]  Shengcai Liao,et al.  Learning Face Representation from Scratch , 2014, ArXiv.

[12]  Rama Chellappa,et al.  Unconstrained face verification using deep CNN features , 2015, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).

[13]  Michael I. Jordan,et al.  Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.

[14]  Yoshua Bengio,et al.  How transferable are features in deep neural networks? , 2014, NIPS.

[15]  Ming Yang,et al.  DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.