Recognition of Reconstructed Frontal Face Images Using FFT-PCA/SVD Algorithm

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.

Evaluation of the DWT-PCA/SVD Recognition Algorithm on Reconstructed Frontal Face Images

“…The findings of the study are consistent with those of Asiedu et al [6] and Singh and Nandi [5]. The DWT-PCA/SVD algorithm is recommended as a suitable algorithm for face image recognition under partial occlusion (half face images).…”

“…The main numerical performance metrics adopted for assessment of the study algorithm (DWT-PCA/SVD) were the average recognition rate and computational time (runtime of the algorithm). According to Asiedu et al [6], the average recognition rate, R avg , of an algorithm is given as…”

“…Image Reconstruction. According to Asiedu et al [6], the left segmented half images can be reconstructed using the following steps:…”

“…Asiedu et al [6] evaluated the performance of principal component analysis with singular value decomposition using fast Fourier transform (FFTPCA/SVD) for the preprocessing algorithm on the reconstructed face database. They found that the recognition rates of the FFT-PCA/SVD algorithm in the recognition of left and right reconstructed face images were 95% and 90%, respectively.…”

Assessing the Effect of Data Augmentation on Occluded Frontal Faces Using DWT-PCA/SVD Recognition Algorithm

“…Abdul-Jabbar [3] showed that preprocessing steps such as image adjustment, histogram equalization, and change in file format when applied to enhance the contrast and the quality of face images in different face recognition algorithms improve the accuracy of recognition up to 30% as compared to using the original database of face images. In the case where half of the face is degraded due to occlusions, several researchers [4,5] have leveraged the bilateral symmetry of face images to reconstruct the full-face images and have used different denoising techniques to enhance image quality. Asiedu et al [4] reconstructed frontal face images from left and right-half images using principal component analysis and singular value decomposition (FFT-PCA/SVD) and employed fast Fourier transforms in the preprocessing stage.…”