Frequency Domain Face Recognition

In the always expanding field of biometrics the choice of which biometric modality or modalities to use, is a difficult one. While a particular biometric modality might offer superior discriminative properties (or be more stable over a longer period of time) when compared to another modality, the ease of its acquisition might be quite difficult in comparison. As such, the use of the human face as a biometric modality presents the attractive qualities of significant discrimination with the least amount of intrusiveness. In this sense, the majority of biometric systems whose primary modality is the face, emphasize analysis of the spatial representation of the face i.e., the intensity image of the face. While there has been varying and significant levels of performance achieved through the use of spatial 2-D data, there is significant theoretical work and empirical results that support the use of a frequency domain representation, to achieve greater face recognition performance. The use of the Fourier transform allows us to quickly and easily obtain raw frequency data which is significantly more discriminative (after appropriate data manipulation) than the raw spatial data from which it was derived. We can further increase discrimination through additional signal transforms and specific feature extraction algorithms intended for use in the frequency domain, so we can achieve significant improved performance and distortion tolerance compared to that of their spatial domain counterparts. In this chapter we will review, outline, and present theory and results that elaborate on frequency domain processing and representations for enhanced face recognition. The second section is a brief literature review of various face recognition algorithms. The third section will focus on two points: a review of the commonly used algorithms such as Principal Component Analysis (PCA) (Turk and Pentland, 1991) and Fisher Linear Discriminant Analysis (FLDA) (Belhumeur et al., 1997) and their novel use in conjunction with frequency domain processed data for enhancing face recognition ability of these algorithms. A comparison of performance with respect to the use of spatial versus processed and un-processed frequency domain data will be presented. The fourth section will be a thorough analysis and derivation of a family of advanced frequency domain matching algorithms collectively known as Advanced Correlation Filters (ACFs). It is in this section that the most significant discussion will occur as ACFs represent the latest advances in frequency domain facial recognition algorithms with specifically built-in distortion tolerance. In the fifth section we present results of more recent research done involving ACFs and face recognition. The final