Optimal multi-kernel SVM classifier with rotation, illumination and scale invariant hybrid DWT-Shearlet based GLCM feature descriptor and its application to face recognition
Affiliations: Department of Computer Science and Engineering, Sharnbasva University Kalaburagi, Karnataka 585103, India | E-mail: sveerashetty123@gmail.com
Correspondence:
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Corresponding author: Department of Computer Science and Engineering, Sharnbasva University Kalaburagi, Karnataka 585103, India. E-mail: sveerashetty123@gmail.com.
Abstract: In computer vision, we must handle with the various structural aspects of image or video data. The texture is one of the most important aspects of this type of data, which is utilised to identify objects or regions of interest in an image. As imaging conditions change, textures inside actual images significantly change in brightness, contrast, size, and skew. To recognise textures in real-world images, a similarity measure that is invariant to these features must be used. Existing recognition techniques did not perform well due to issues such as illumination, scale, and subject rotation. To address this issue, invariant feature representation methods are being developed to generate features that are insensitive to such variations. In this paper, we proposed a robust hybrid feature descriptor and predicted the faces under illumination, scale, and pose variations using an optimum multi-kernel support vector machine. Additionally, the suggested robust hybrid feature descriptor is enhanced by combining a hybrid transform composed of discrete wavelet and discrete shearlet transforms with some image statistical textural data. The proposed face recognition system is implemented in MATLAB, and analysed using various parameters to show proposed methods improved performance as compared to the state of the art methods.
Keywords: Descriptors, multi-kernel SVM, shearlet, face recognition, texture analysis
