Abstract
Automatic gender classification is accepting much research enthusiasm because of its numerous applications. Perceiving the sex of a person is a challenging task because of huge variations in posture, illumination, occlusion, scaling, and facial expression. In this manuscript, we propose a novel approach for multi-view gender recognition by considering the facial shape and texture features. Dominant rotated local binary pattern (DRLBP) and rotation invariant local phase quantization (RILPQ) descriptors are utilized for extracting texture features. Pyramid histogram of oriented gradient (PHOG) descriptors is utilized for extracting shape feature. RBF kernel SVM is used to classify the gender classes, namely male and female. Experimental results indicate that the fusion of DRLBP, RILPQ, and PHOG utilizing an SVM with RBF kernel outperforms state of the art on the LFW, Adience, and FEI face database.
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Annie Micheal, A., Geetha, P. (2019). Combined Feature Extraction for Multi-view Gender Recognition. In: Satapathy, S., Bhateja, V., Das, S. (eds) Smart Intelligent Computing and Applications . Smart Innovation, Systems and Technologies, vol 104. Springer, Singapore. https://doi.org/10.1007/978-981-13-1921-1_22
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DOI: https://doi.org/10.1007/978-981-13-1921-1_22
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