Performance evaluation of edge feature extracted using sparse banded matrix filter applied for face recognition

Abstract

This paper deals with the performance evaluation of sparse banded matrix filter applied for Face recognition. Edges extracted using the sparse banded matrix filter (ABFilter) is used as a feature descriptor for face recognition. The classification is done using Random Kitchen Sink which is accessed through GURLS library and also classified using Support Vector Machines (SVM). The experimental evaluation of sparse banded matrix filter is done on a standard face database (Yale). Edge detection is the process of locating the sharp discontinuity in an image. It is a basic tool which is used in many image processing applications such as face recognition. In this paper, we have compared the performance of sparse banded matrix filter with existing edge detecting filters such as Sobel, Prewitt, Canny and Robert. Though many filters exist for edge detection, sparse banded matrix filter is known for the edge detection with minimal discontinuity. The experimental evaluation shows that the edge feature descriptors of Yale face database obtained using sparse banded matrix filter provides 88 % accuracy using GURLS and 81% using SVM.