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A Hybrid Approach for Gender Classification of Web Images

International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 54 - Number 7
Year of Publication: 2012
Muhammad Usman Khan
Hafiz Adnan Habib
Nasir Saleem

Muhammad Usman Khan, Hafiz Adnan Habib and Nasir Saleem. Article: A Hybrid Approach for Gender Classification of Web Images. International Journal of Computer Applications 54(7):11-16, September 2012. Full text available. BibTeX

	author = {Muhammad Usman Khan and Hafiz Adnan Habib and Nasir Saleem},
	title = {Article: A Hybrid Approach for Gender Classification of Web Images},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {54},
	number = {7},
	pages = {11-16},
	month = {September},
	note = {Full text available}


In recent times, gender recognition of facial images has achieved lots of attraction. It can be useful in many places e. g. security, web searching, human computer interaction etc. In this paper, an approach containing both face detection and gender classification tasks has been proposed. In face detection part, Haar features have been chosen to present appearance features along with Ada-Boost technique to target strong and powerful features in cascaded form. For gender classification, Bayesian Classifier has been used where image is analyzed in blocks/patches form. The blocking technique is same as used in DCT approach. Experimental results have shown that proposed approach is effective and robust with changes in pose (some degree), expressions and illumination.


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