Abstract
Face recognition has gained prominence among the various biometric-based methods (such as fingerprint and iris) due to its noninvasive characteristics. Modern face recognition modules/algorithms have been successful in many application areas (access control, entertainment/leisure, security system based on biometric data, and user-friendly human-machine interfaces). In spite of these achievements, the performance of current face recognition algorithms/modules is still inhibited by varying environmental constraints such as occlusions, expressions, varying poses, illumination, and ageing. This study assessed the performance of Principal Component Analysis with singular value decomposition using Fast Fourier Transform (FFT-PCA/SVD) for preprocessing the face recognition algorithm on left and right reconstructed face images. The study found that average recognition rates for the FFT-PCA/SVD algorithm were 95% and 90% when the left and right reconstructed face images are used as test images, respectively. The result of the paired sample t-test revealed that the average recognition distances for the left and right reconstructed face images are not significantly different when FFT-PCA/SVD is used for recognition. FFT-PCA/SVD is recommended as a viable algorithm for recognition of left and right reconstructed face images.
Highlights
Recognizing people using face images has gained prominence among the various biometric-based methods due to its comparative advantage of being nonintrusive and less cooperative
The average recognition rates for the Fast Fourier Transform (FFT)-Principal Component Analysis (PCA)/singular value decomposition (SVD) algorithm were 95% and 90% when left and right reconstructed face images are used as test images, respectively
The statistical assessment revealed that there is no significant difference between the average recognition distances for the left and right reconstructed images
Summary
Recognizing people using face images has gained prominence among the various biometric-based methods (such as fingerprint and iris) due to its comparative advantage of being nonintrusive and less cooperative (of subjects under study). This task is carried out by humans. Bilateral symmetry is a property of many natural objects including the human face [4] Leveraging this property, the performances of holistic face representationbased algorithms have been evaluated on the left, right, and average half faces based on symmetry scores [5, 6]
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