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Multi-texture local ternary pattern for face recognition
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Paper Abstract

In imagery and pattern analysis domain a variety of descriptors have been proposed and employed for different computer vision applications like face detection and recognition. Many of them are affected under different conditions during the image acquisition process such as variations in illumination and presence of noise, because they totally rely on the image intensity values to encode the image information. To overcome these problems, a novel technique named Multi-Texture Local Ternary Pattern (MTLTP) is proposed in this paper. MTLTP combines the edges and corners based on the local ternary pattern strategy to extract the local texture features of the input image. Then returns a spatial histogram feature vector which is the descriptor for each image that we use to recognize a human being. Experimental results using a k-nearest neighbors classifier (k-NN) on two publicly available datasets justify our algorithm for efficient face recognition in the presence of extreme variations of illumination/lighting environments and slight variation of pose conditions.

Paper Details

Date Published: 1 May 2017
PDF: 6 pages
Proc. SPIE 10203, Pattern Recognition and Tracking XXVIII, 102030H (1 May 2017); doi: 10.1117/12.2263735
Show Author Affiliations
Almabrok Essa, Univ. of Dayton (United States)
Vijayan Asari, Univ. of Dayton (United States)


Published in SPIE Proceedings Vol. 10203:
Pattern Recognition and Tracking XXVIII
Mohammad S. Alam, Editor(s)

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