Abstract
The face is the second most important biometric part of the human body, next to the finger print. Recognition of face image with partial occlusion (half image) is an intractable exercise as occlusions affect the performance of the recognition module. To this end, occluded images are sometimes reconstructed or completed with some imputation mechanism before recognition. This study assessed the performance of the principal component analysis and singular value decomposition algorithm using discrete wavelet transform (DWT-PCA/SVD) as preprocessing mechanism on the reconstructed face image database. The reconstruction of the half face images was done leveraging on the property of bilateral symmetry of frontal faces. Numerical assessment of the performance of the adopted recognition algorithm gave average recognition rates of 95% and 75% when left and right reconstructed face images were used for recognition, respectively. It was evident from the statistical assessment that the DWT-PCA/SVD algorithm gives relatively lower average recognition distance for the left reconstructed face images. DWT-PCA/SVD is therefore recommended as a suitable algorithm for recognizing face images under partial occlusion (half face images). The algorithm performs relatively better on left reconstructed face images.
Highlights
The heightened interest of researchers in the subject of face recognition is mainly due to various application areas of efficient and resilient face recognition modules
The algorithm (DWT-PCA/singular value decomposition (SVD)) recorded three mismatches or wrong matches when the right reconstructed face images were used as test images for recognition from the Japanese Female Facial Expressions (JAFFE)-database
Overall, the discrete wavelet transform (DWT)-PCA/SVD algorithm recorded two mismatches or wrong matches when the left reconstructed face images were used for recognition and five mismatches or wrong matches when the right reconstructed face images were used as test images for recognition
Summary
The heightened interest of researchers in the subject of face recognition is mainly due to various application areas of efficient and resilient face recognition modules These include bankcard identification, security monitoring, access control, and surveillance control systems. According to Turk and Pentland [2], face recognition algorithms’ performances are restricted by constrained environments. Some of these constraints are illuminations, ageing, occlusion of face, and varying head tilt and facial expressions. A special case of partially occluded faces occurs where either the left or right face is occluded or segmented, and the remaining half (nonoccluded part) is used for recognition This can be regarded as performing face recognition using half face images [4]
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