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Discriminative Robust Local Binary Pattern Based Face Recognition From A Single Sample Per Person


D.R.Monisha, R.Pavithra


Vol. 16  No. 3  pp. 57-62


Nowadays face recognition plays an important role in today’s world. The core objective of this project is to extract the facial features using the local appearance based method for the accurate face identification with single sample per class. The face biometric based person identification plays a major role in wide range of applications such as Airport security, Driver’s license, Passport, Voting System, Surveillance. This project presents face recognition based on Difference of Gaussian and feature extraction using Discriminative Robust Local Binary pattern Pattern(DRLBP)approach. The Median filter is used to extract the hybrid features and the pyramids are generated after the face granulation. Then, DoG pyramid will be formed from successive iterations of Gaussian images. By this granulation, facial features are segregated at different resolutions to provide edge information, noise, smoothness and blurriness present in a face image. In feature extraction stage, this binary face template act like a mask to extract local texture information using Discriminative Robust Local binary pattern. This method is efficient to face recognition since it is less sensitive to illumination and scaling. . It reduces the computational time complexity and space complexity. This proposed approach reduces the computation time and also increases the efficiency.


Single sample per class, Median Filter, DoG pyramid, Discriminative Robust Local Binary Pattern.