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ISSN Print: 2394-7500, ISSN Online: 2394-5869, CODEN: IJARPF

Impact Factor: RJIF 5.2

International Journal of Applied Research

Vol. 3, Issue 4, Part C (2017)

Semantic annotation for gender identification using support vector machine

Author(s)
Sayantani Ghosh, Debnath bhattacharyya, Tai-Hoon KIM and Samir K Bandyopadhyay
Abstract
Gender classification is a binary classification problem, which can be stated as inferring female or male from a collection of facial images. Although there exist different methods for gender classification, such as gait, iris, hand shape and hair, yet the prominent methods to achieve the goal is based on facial features. Support Vector Machines (SVMs) are investigated for visual gender classification with low resolutions thumbnail ̈faces (21-by-12 pixels) processed from 1,755 images from the FERET face database. The performance of SVMs (3.4% error) is shown to be superior to traditional pattern classifiers (Linear, Quadratic, Fisher Linear Discriminant, Nearest-Neighbor) as well as more modern techniques such as Radial Basis Function (RBF) classifiers and large ensemble-RBF networks. In this paper SVM basic kernel function has been employed firstly to detect and classify the human gender Image into two labels i.e. (1) male and (2) female. These functions read as input the feature(s) of the human facial image. It uses two modes as ‘Training Mode’ and ‘Classification Mode’ and gender identification are made for semantic annotation of videos. The algorithm has been executed on elementary features of human facial image i.e. eyes, nose, lips and their all possible combinations. Finally based on the accuracy percentage of the computed result the admissible result of the Kernel Functions has been realized. The gender classifier achieves over 96% accuracy.
Pages: 147-160  |  495 Views  16 Downloads
How to cite this article:
Sayantani Ghosh, Debnath bhattacharyya, Tai-Hoon KIM and Samir K Bandyopadhyay. Semantic annotation for gender identification using support vector machine. International Journal of Applied Research. 2017; 3(4): 147-160.
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