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A Classification of Emotion and Gender Using Local Biorthogonal Binary Pattern from Detailed Wavelet Coefficient Face Image

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 472)

Abstract

This work investigates a framework which identifies gender and emotion of the person from the face image. Gender with their expressions has a vital role in the suspect detection systems. The proposed system aids in identification of a person with their gender as male and female. Also detects gender’s expression as joy and sadness. In this paper, wavelet detailed coefficient and Biorthogonal family-based system have been used simultaneously to identify gender and emotion of a face image. Detailed image local Biorthogonal binary pattern (DILBBP) has been applied for feature extraction and for classification purpose; SVM is applied. Experiments are performed on publicly available standard FERET, INDIAN FACE, and AR FACE databases. Proposed work gives acceptable classification and recognition results with less computational time.

Keywords

Gender classification Emotion detection Detailed image local biorthogonal binary pattern (DILBBP) Support vector machine 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceGGITSJabalpurIndia

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