Face Recognition Under Angular Constraint Using Discrete Wavelet Transform And Principal Component Analysis With Singular Value Decomposition

Abstract

The complexity of a face’s components originates from the incessant variations in the facial component that occur with respect to time. Notwithstanding of these variations, humans recognise a person very easily using physical characteristics such as faces, voice, gait, etc. In human interactions, the articulation and perception of constraints; like head-poses, facial expression form a communication channel that is additional to voice and that carries crucial information about mental, emotional and even physical states of a conversation. Automatic face recognition deals with extracting these essential features from an image, placing them into a suitable representation and performing some kind of recognition on them. This study presents a statistical assessment of the performance of modify Discrete Wavelet Transform and Principal Component Analysis with Singular Value Decomposition (DWT-PCA/SVD) under angular constraints (4°,8°,12°,16°,20°,24°,28°and 32°). 80 head-pose images from 10 individuals were captured into the study database. The study dataset extracted from Massachusetts Institute of Technology Database (2003-2005) were considered for recognition runs. A Friedman Sum Rank was used to ascertain whether significant difference exist between the median recognition distances of the varying constraints from their straight-pose ( 0°). Recognition rate and runtime was adopted as the numerical evaluation methods to assess the performance of the study algorithm. All numerical and statistical computations were done using Matlab. The results of the Friedman Sum Rank test show that the higher the degrees of head-pose, the larger the recognition distances and that at 20° and above, the recognition distances become profoundly larger compared to the 4° head-pose. The numerical evaluations show that, DWT-PCA/SVD face recognition algorithm has an appreciable average recognition rate (87.5%) when used to recognise face images under angular constraints. Also the recognition rate decreases for head-poses greater than 20°. DWT is recommended as a feasible noise removal tool that should be implemented during image pre-processing phase.