Recovery from Occlusion in Deep Feature Space for Face Recognition Feng Cen

Motivation Deep convolutional neural networks: Outperform human vision for face verification on LFW database Fail to handle contiguous occlusion Sparse representation classifier Classical method for face images with occlusion Image space or linear feature space Difficult to deal with pose variations, facial expressions, and illumination changes etc. D: training dictionary

Observation

Assumption

Algorithm Residual:

Algorithm Dimension reduction with PCA Normalization of the dictionary atom Normalization of the residual with the l2 -norm of gallery coding coefficients

Experiments: AR Database Parameters Auxiliary dictionary

Experiments: AR Database Auxiliary dictionary generation

Experiments: AR Database Performance

Experiments: AR Database A single training sample per person

Experiments: FERET database Training: 150 subjects, non-occlusion ‘ba’, ‘bj’, ‘bk’ Testing: 150 subjects, block occlusion Auxiliary dictionary: other 44 subjects

Time comsumption Less than 0.4s per image – Intel i7 CPU Dictionary coding: <2ms CNNs : <0.4s without GPU acceleration

Conclusion Real time Robust to illumination changes, facial expressions, pose variations and contiguous occlusions A single training sample per person

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