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Gender Recognition Using Fusion of Spatial and Temporal Features

  • Suparna Biswas
  • Jaya Sil
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 27)

Abstract

In the paper, a gender recognition scheme has been proposed based on fusion of spatial and temporal features. As a first step, face from the image is detected using Viola Jones method and then spatial and temporal features are extracted from the detected face images. Spatial features are obtained using Principal Component Analysis (PCA) while Discrete Wavelet Transform (DWT) has been applied to extract temporal features. In this paper we investigate the fusion of both spatial and temporal features for gender classification. The feature vectors of test images are obtained and classified as male or female by Weka tool using 10 fold cross validation technique. To evaluate the proposed scheme FERET database has been used providing accuracy better than the individual features. Experimental result shows 9.77% accuracy improvement with respect to spatial domain recognition system.

Keywords

Feature extraction Gender classification Discrete wavelet transform Principal component analysis 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Electronics & communication EngineeringGurunanak Institute of TechnologyKolkataIndia
  2. 2.Department of Computer Science and TechnologyBengal Engineering & Science UniversitykolkattaIndia

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