Improved Face Verification with Simple Weighted Feature Combination

Since the appearance of deep learning, face verification (FV) has made great progress with large scale datasets, well-designed networks, new loss functions, fusion of models and metric learning methods. However, incorporating all these methods obviously takes a lot of time both at training and testing stages. In this paper, we just select training images randomly without any clean and alignment procedure. Then we propose a simple weighted average method which combines features of the last two layers with different weights on the modified VGGNet, named as CB-VGG. It is significantly reducing the complexity of time that one model can be treated as two models. LMNN is used as a post-processing procedure to improve the discrimination of the combined features. Our experiments show relatively competitive results on LFW, CFP, and CACD datasets.

[1]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Xiaogang Wang,et al.  Deep Learning Face Representation from Predicting 10,000 Classes , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Jian Sun,et al.  Bayesian Face Revisited: A Joint Formulation , 2012, ECCV.

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

[5]  Xiaogang Wang,et al.  DeepID3: Face Recognition with Very Deep Neural Networks , 2015, ArXiv.

[6]  Xiaoming Liu,et al.  Multi-Task Convolutional Neural Network for Face Recognition. , 2017 .

[7]  Shengcai Liao,et al.  A Fast and Accurate Unconstrained Face Detector , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Chang Huang,et al.  Targeting Ultimate Accuracy: Face Recognition via Deep Embedding , 2015, ArXiv.

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

[10]  Tal Hassner,et al.  Do We Really Need to Collect Millions of Faces for Effective Face Recognition? , 2016, ECCV.

[11]  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.

[12]  Chu-Song Chen,et al.  Face Recognition and Retrieval Using Cross-Age Reference Coding With Cross-Age Celebrity Dataset , 2015, IEEE Transactions on Multimedia.

[13]  Carlos D. Castillo,et al.  L2-constrained Softmax Loss for Discriminative Face Verification , 2017, ArXiv.

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

[15]  Yuxiao Hu,et al.  MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition , 2016, ECCV.

[16]  Yu Qiao,et al.  Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[18]  Liming Chen,et al.  DeepVisage: Making Face Recognition Simple Yet With Powerful Generalization Skills , 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).

[19]  Carlos D. Castillo,et al.  Triplet probabilistic embedding for face verification and clustering , 2016, 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS).

[20]  Xiaogang Wang,et al.  Deep Learning Face Representation by Joint Identification-Verification , 2014, NIPS.

[21]  Yu Qiao,et al.  A Discriminative Feature Learning Approach for Deep Face Recognition , 2016, ECCV.

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

[23]  Zhenan Sun,et al.  A Lightened CNN for Deep Face Representation , 2015, ArXiv.

[24]  Carlos D. Castillo,et al.  Frontal to profile face verification in the wild , 2016, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).

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

[26]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[27]  Rama Chellappa,et al.  Fisher vector encoded deep convolutional features for unconstrained face verification , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[28]  Xiaogang Wang,et al.  Deeply learned face representations are sparse, selective, and robust , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).