Recognition of Faces using Efficient Multiscale Local Binary Pattern and Kernel Discriminant Analysis in Varying Environment
- 1 Priyadarshini College of Engg, India
- 2 Government Polytechnic, India
Copyright: © 2020 Sujata G. Bhele and V.H. Mankar. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Face recognition involves matching face images with different environmental conditions. Matching face images with different environmental conditions is not a easy task. Also matching face images considering variations such as changing illumination, pose, facial expression and that with uncontrolled conditions becomes more difficult. This paper focuses on accurately recognizing face images considering all the above variations. The proposed system is based on collecting features from face images using Multiscale Local Binary pattern (MLBP) with eight orientations out of 59 crucial ones and then finding similarity using a kernel linear discriminant analysis. Literature suggested that MLBP can give up to 256 orientations for a single radius considered around a pixel and its neighborhood. The paper uses only 8 orientations for a single radius and four such radii (1, 3, 5 and 7) are considered around a single pixel with (8x4) 32 histogram features thus reducing the computational complexity. Various face image databases are considered in this paper namely, Labeled Faces in Wild (LFW), Japanese Female Facial Expression (JAFFE), AR and Asian. Results showed that the proposed system correctly identified 9 out of 10 subjects. The proposed system involves preprocessing including alignment and noise reduction using a Gaussian filter, feature extraction using MLBP based histograms and matching based on kernel linear discriminant analysis.
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- Face Recognition
- Uncontrolled Conditions
- Local Binary Pattern (LBP)
- Multi-Scale Local Binary Pattern (MLBP)
- Histogram Features
- Discriminant Analysis