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논문검색

Multi-view Gender Classification using hybrid Transformed Features

초록

영어

In this paper, a two step efficient pose based gender classification technique has been presented. In first step, the proposed technique uses different type of features for pose classification which classifies the input image as frontal or side pose image. Based on pose classification result, gender classification is performed in the second step. Two different types of gender classifiers have been proposed for both side pose and frontal images which use the image and pose classification result as input and classifies the image as male or female. The proposed technique is tested over standard datasets using well known quantitative measures. Results show that the proposed technique gives superior performance than the existing techniques.

목차

Abstract
 1. Introduction
 2. Proposed Method
 3. Experimental Results & Discussion
 4. Conclusion
 Acknowledgements
 References

저자정보

  • Muhammad Nazir National University of Computer and Emerging Sciences, FAST AK Brohi Road H-11/4, Islamabad, Pakistan
  • Anwar. M. Mirza Department of Computer Engineering, College Of Computer and Information Sciences, King Saud University, P.O Box. 2454, Riyadh 1154, Saudi Arabia

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