Face Recognition using Tensors of Census Transform Histograms from Gaussian Features Maps

This paper presents a new approach for face recognition based on the fusion of tensors of census transform histograms from Local Gaussian features maps. Local Gaussian feature maps encode the most relevant information from Gaussian derivative features. Census Transform (CT) histograms are calculated and concatenated to form a tensor for each class of Gaussian map. Multi-linear Principal Component Analysis (MPCA) is applied to each tensor to reduce the number of dimensions as well as the correlation between neighboring pixels due to the Census Transform. We then train Kernel Discriminative Common Vectors (KDCV) to generate a discriminative vector using the results of the MPCA. Results of recognition using MPCA of tensors-CT histograms from Gaussian features maps with KDCV is shown to compare favorably with competing techniques that use more complex features maps like for example Gabor features maps in the FERET and Yale datasets. Additional experiments were done in the Yale B+ extended Yale B Faces dataset to show the performance of Gaussian features map with hard illumination changes.