Recognition Of Face Images Under Varying Expressions Using Discrete Wavelet Transform And Principal Component Analysis With Singular Value Decomposition

ABSTRACT The face is the most widely used part of the human body for recognition. Automated face recognition is all about transferring an inherent trait in humans into machines by supplying the machine with enough information. Face recognition has gained much attention over the past few decades and it is used in numerous application areas which includes but not limited to information privacy, law enforcement and assess management. The study presents an assessment of the Discrete Wavelet Transform and Principal Component Analysis with Singular Value Decomposition under varying facial expressions. One hundred and eighty-two (182) frontal images from the Cohn Kanade AU-Coded database were used in the study database. These images were obtained from twenty-six persons and were considered for recognition runs. A repeated measures design was used to determine whether there existed significant differences in the average recognition distances of the varying facial expressions from their neutral pose. Recognition rate and runtime were employed as the numerical evaluation methods to assess the performance of the study algorithm. The numerical and statistical computations were done using Matlab. The results of the Repeated Measures revealed that there existed significant differences in the average recognition distances from their neutral pose. The numerical evaluations also showed that, DWT-PCA/SVD face recognition has a remarkable average recognition rate (98.7%) under varying expressions and an average run time of 10.5 seconds which was influenced by the DWT used at the preprocessing stage. Therefore, DWT is recommended as a viable noise removal tool that should be implemented during image preprocessing phase of face recognition systems.