Statistical Evaluation of Face Recognition Techniques under Variable Environmental Constraints

Abstract

Experiments have shown that, even one to three day old babies are able to distinguish between known faces (Chiara, Viola, Macchi, Cassia, & Leo, 2006). So how hard could it be for a computer? It has been established that face recognition is a dedicated process in the brain (Marqueś, 2010). Thus the idea of imitating this skill inherent in human beings by machines can be very rewarding though the idea of developing an intelligent and self-learning system may require supply of sufficient information to the machine. This study proposes multivariate statistical evaluation of the recognition performance of Principal Component Analysis and Singular Value Decomposition (PCA/SVD) and a Whitened Principal Component Analysis and Singular Value Decomposition algorithms (Whitened PCA/SVD) under varying environmental constraints. The Repeated Measures Design, Paired Comparison test, Box's M test and Profile Analysis were used for performance evaluation of the algorithms on the merit of efficiency and consistency in recognizing face images with variable facial expressions. The study results showed that, PCA/SVD is consistent and computationally efficient when compared to Whitened PCA/SVD.

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

“…where t run is the number of experimental runs, n i cr is the number of correct recognitions in the ith run, and n tot is the total number of faces being tested in each run [7]. The average error rate, E avg , is defined as 100 − R avg .…”

“…They reported a significantly higher recognition rate using the average half face for both men and women compared to the full face. Asiedu et al [7] applied the PCA/SVD algorithm on full faces under varying facial expressions. They concluded that the algorithm was most consistent and efficient under varying expressions and achieved appreciable performance with an average recognition rate of 92.86%.…”

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

“…As indicated earlier, the DWT-PCA/SVD algorithm was used to train the image database to extract unique face features for recognition. e primary objective of the PCA/SVD feature extraction mechanism is to find a set of n orthonormal vectors, ξ j , which best describes the distribution of the image data [28]. e k-th vector ξ k is chosen such that…”

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

“…According to Glynn [14], the FFT algorithm reduces the computational burden to O(N log N) arithmetic operations. Zhang et al [15] and Asiedu et al [16] demonstrated that the application of FFT in the image preprocessing stage improves the recognition system.…”

“…We now present the mathematical underpins of the face feature extraction mechanism, PCA/SVD, as described by Asiedu et al [16].…”