International Journal of Statistics and Probability; Vol. 4, No. 4; 2015 ISSN 1927-7032 E-ISSN 1927-7040 Published by Canadian Center of Science and Education 93 Statistical Evaluation of Face Recognition Techniques under Variable Environmental Constraints Louis Asiedu 1 , Atinuke O. Adebanji 2 , Francis Oduro 3 & Felix O. Mettle 4 1 Department of Statistics, University of Ghana, Legon-Accra, Ghana 2 Department of Mathematics, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana 3 Department of Mathematics, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana 4 Department of Statistics, University of Ghana, Legon-Accra, Ghana Correspondence: Louis Asiedu, Department of Statistics, University of Ghana, Legon-Accra, Ghana. Tel: 233-543-426-707. E-mail: lasiedu@ug.edu.gh Received: August 1, 2015 Accepted: August 19, 2015 Online Published: October 9, 2015 doi:10.5539/ijsp.v4n4p93 URL: http://dx.doi.org/10.5539/ijsp.v4n4p93 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 (Marque´s, 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. Keywords: Principal Component Analysis, Singular Value Decomposition, whitening, multivariate, repeated measures design, Paired Comparison, Box’s M and profile analysis. 1. Introduction Face recognition is an easy task for humans. Although the ability to infer the intelligence or character from facial appearance is suspect, the human ability to recognize faces is remarkable (Turk & Pentland, 1991). According to Rahman (2013), the intricacy of a face features originate from continuous changes in the facial features that take place over time. Regardless of these changes, we are able to recognize a person very easily. In recent years, face recognition techniques have gained significant attention from researchers partly because face recognition is non-invasive with a sense of primary identification. One of the main driving factors for face recognition is the ever growing number of applications that an efficient and resilient recognition technique addresses; for example, security systems based on biometric data, criminal identification, missing children identification, passport/driver license, voter identification and user-friendly human-machine interfaces. An example of the later category is smart rooms, which use cameras and microphones arrays to detect the presence of humans, decide on their identity and then react according to the predefined set of preferences for each person. Currently, all face recognition techniques work in either of the two ways. One is local face recognition system which uses facial features (nose, mouth, eyes) of a face. That is to consider the fiducial points in the face to associate the face with a person. The local-feature method computes the descriptor from parts of the face and gathers information into one descriptor. Some local-feature methods are, Local Feature Analysis (LFA), Garbor Features, Elastic Bunch Graph Matching (EBGM) and Local Binary Pattern Feature Agrawal et al., (2014). The second approach or global face recognition system uses the whole face to identify a person. The principle of whole face method is to construct a subspace using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Independent Component Analysis (ICA), Random Projection (RP), or Non-negative Matrix