Abstract:
Many architectures of face recognition modules have been developed to tackle the challenges posed by varying environmental
constraints such as illumination, occlusions, pose, and expressions. These recognition systems have mainly focused on a single
constraint at a time and have achieved remarkable successes. However, the presence of multiple constraints may deteriorate the
performance of these face recognition systems. In this study, we assessed the performance of Principal Component Analysis
and Singular Value Decomposition using Discrete Wavelet Transform (DWT-PCA/SVD) for preprocessing face recognition
algorithm on multiple constraints (partially occluded face images acquired with varying expressions). Numerical evaluation of
the study algorithm gave reasonably average recognition rates of 77.31% and 76.85% for left and right reconstructed face
images with varying expressions, respectively. A statistically significant difference was established between the average
recognition distance of the left and right reconstructed face images acquired with varying expressions using pairwise
comparison test. The post hoc analysis using the Bonferroni simultaneous confidence interval revealed that the significant
difference established through the pairwise comparison test was mainly due to the sad expressions. Although the performance
of the DWT-PCA/SVD algorithm declined as compared to its performance on single constraints, the algorithm attained
appreciable performance level under multiple constraints. The DWT-PCA/SVD recognition algorithm performs reasonably
well for recognition when partial occlusion with varying expressions is the underlying constraint.
