Feature fusion by using LBP, HOG, GIST descriptors and Canonical Correlation Analysis for face recognition

Face recognition is the most active research topics in machine vision because of its highly secured demands. The fusion of multiple features can enhance the accuracy of face recognition systems instead of using only one type of feature. However, this leads to increase the storage and processing time. In this work, we apply feature fusion by using Canonical Correlation Analysis to concatenate two different feature sources for coding a facial image. Three popular descriptors (LBP, HOG, GIST) have been investigated for extracting facial features based on block division.