A competent multimodal recognition using imperfect region based face and gait cues using Median-LBPF and Median-LBPG based PCA followed by LDA

Abstract

Today, all conventional computer based multimodal biometric system for human recognition that combines face and gait cues have primarily focused on recognition from perfect face and gait images. There are circumstances in which perfect face and gait images may not be obtainable which means probe images are imperfect. This paper proposes new methods Median Local Binary Pattern of Face image (Median-LBPF) and Gait image (Median-LBPG) to extract the features of imperfect face and gait images efficiently for better recognition. Initially the given imperfect face and gait images are divided into six overlapped regions called top, bottom, left, right, vertical center, horizontally center overlapped half images. The features of these six overlapped regions of imperfect face and gait images in the spatial domain are extracted by using Median-LBPF and Median-LBPG. Subsequently the dimensionality of the feature sets are reduced by a two stage feature reduction algorithms Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA). Next for classification, Euclidean distance measure is used to calculate the minimum of minimum distance between the six overlapped regions of given imperfect face and gait probe images and the corresponding regions of all six overlapped regions in the training sets separately called face decision and gait decision. Finally the face decision and gait decision are fused at decision level for recognition. The proposed methods are tested by using publically available data sets ORL face and CASIA gait. The experimental results show that features of a region of face and gait images are adequate for recognition and its average recognition performance is same as perfect face and gait images.