A Double Filtered GIST Descriptor for Face Recognition

We are in the era of wearable technologies, biometrics and multi-factor authentication, where one's face is increasingly becoming a digital identifier for access control and authentication. Compared to the other biometrics such as Fingerprint, Iris and Palm print, Face Recognition (FR) has the distinctness of being non-intrusive, and has hence garnered substantial mainstream attention. As the devices that incorporate FR are evolving into miniaturization, there is a need to develop more robust algorithms that are computationally less expensive. Hence, in an effort to provide a computationally effective FR methodology, we extend the cost-effective GIST descriptor that was designed primarily for object recognition, to be commensurate with FR. This paper proposes a double filtered GIST based descriptor for FR that embodies certain inventive preprocessing steps such as edge detection via Prewitt descriptor, DCT and IDCT transformation to reduce noise prior to feature description with GIST. We will demonstrate by performing extensive experimentations on the ORL and IIT-K face databases that the proposed methodology is capable of effectively performing FR, even in the presence of sharp variations in a number of crucial FR parameters among the faces being compared.