Two Dimensionality Reduction Techniques for SURF Based Face Recognition

In the gargantuan domain of biometrics, the most prominent field is face recognition. We are our faces, in a way we are not our social networking profiles, our legal names and Aadhaar identification number. Even though voluminous data are collected online about most individuals, for instance on web using browser cookies, ip addresses, MAC addresses or email addresses. Almost everything that represents an individual is merely an untidy collection or pile of numbers and letters. All of these can be changed with some cost or sacrifice. Today, we hear that the victims of fraud can apply for obtaining new unique identifier(s). Despite these, there remains one unique identifier that's different from these and that is our face. It's arduous to change it beyond recognition, if it's even feasible. That is, face recognition bind data about us to us only. Thus, face recognition aids law enforcement agencies as a crime-fighting tool to recognize people based on facial traits. The recent stoor of this field has shown its importance in real time applications. This has created an exponential impact on the research work being carried out in this field over the last few decades. In the recent past binary descriptor based techniques like SIFT, SURF, etc are being widely deployed for recognition systems. Keeping this as focal point, the paper proposes two dimensionality reduction techniques namely SVD (Singular Value Decomposition) and PCA (Principal Component Analysis) for SURF based face recognition. The results of simulations conducted on four exemplar datasets show that the SURF-SVD method is more efficient for face recognition when compared with the other existing methods including the SURF-PCA method.