Facial image classification based on age and gender

Thakshila R. Kalansuriya; Anuja T. Dharmaratne

doi:10.1109/ICTer.2013.6761153

Facial image classification based on age and gender

Thakshila R. Kalansuriya, Anuja T. Dharmaratne

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

6 Citations (Scopus)

Abstract

Automatic face identification and verification from facial images attain good accuracy with large sets of training datawhile face attribute recognition from facial images still remainchallengeable. We propose a methodology for automatic age andLKgender classification based on feature extraction from facial, images, namely, primary and secondary features. Our methodology · includes three main iterations: Preprocessing, Feature extraction and Classification. Our solution is able to classify images in different lighting conditions and different illumination conditions. Classification is done using Artificial Neural Networks according to the different shape and texture variations of wrinkles on face images.

Original language	English
Title of host publication	2013 International Conference on Advances in ICT for Emerging Regions
Pages	44-50
Number of pages	7
DOIs	https://doi.org/10.1109/ICTer.2013.6761153
Publication status	Published - 2013
Externally published	Yes
Event	International Conference on Advances in ICT for Emerging Regions 2013 - Colombo, Sri Lanka Duration: 11 Dec 2013 → 15 Dec 2013 Conference number: 14th https://ieeexplore.ieee.org/xpl/conhome/6747516/proceeding (Proceedings)

Conference

Conference	International Conference on Advances in ICT for Emerging Regions 2013
Abbreviated title	ICTer 2013
Country	Sri Lanka
City	Colombo
Period	11/12/13 → 15/12/13
Internet address	https://ieeexplore.ieee.org/xpl/conhome/6747516/proceeding (Proceedings)

Keywords

Age classification
Feature extraction
Gender classification
Texture
Wrinkles

Access to Document

10.1109/ICTer.2013.6761153

Link to publication in Scopus

Cite this

@inproceedings{06d1f0a4726844578ea5e1b83d3c83a5,

title = "Facial image classification based on age and gender",

abstract = "Automatic face identification and verification from facial images attain good accuracy with large sets of training datawhile face attribute recognition from facial images still remainchallengeable. We propose a methodology for automatic age andLKgender classification based on feature extraction from facial, images, namely, primary and secondary features. Our methodology · includes three main iterations: Preprocessing, Feature extraction and Classification. Our solution is able to classify images in different lighting conditions and different illumination conditions. Classification is done using Artificial Neural Networks according to the different shape and texture variations of wrinkles on face images.",

keywords = "Age classification, Feature extraction, Gender classification, Texture, Wrinkles",

author = "Kalansuriya, {Thakshila R.} and Dharmaratne, {Anuja T.}",

note = "Copyright: Copyright 2014 Elsevier B.V., All rights reserved.; International Conference on Advances in ICT for Emerging Regions 2013, ICTer 2013 ; Conference date: 11-12-2013 Through 15-12-2013",

year = "2013",

doi = "10.1109/ICTer.2013.6761153",

language = "English",

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booktitle = "2013 International Conference on Advances in ICT for Emerging Regions",

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AU - Dharmaratne, Anuja T.

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N2 - Automatic face identification and verification from facial images attain good accuracy with large sets of training datawhile face attribute recognition from facial images still remainchallengeable. We propose a methodology for automatic age andLKgender classification based on feature extraction from facial, images, namely, primary and secondary features. Our methodology · includes three main iterations: Preprocessing, Feature extraction and Classification. Our solution is able to classify images in different lighting conditions and different illumination conditions. Classification is done using Artificial Neural Networks according to the different shape and texture variations of wrinkles on face images.

AB - Automatic face identification and verification from facial images attain good accuracy with large sets of training datawhile face attribute recognition from facial images still remainchallengeable. We propose a methodology for automatic age andLKgender classification based on feature extraction from facial, images, namely, primary and secondary features. Our methodology · includes three main iterations: Preprocessing, Feature extraction and Classification. Our solution is able to classify images in different lighting conditions and different illumination conditions. Classification is done using Artificial Neural Networks according to the different shape and texture variations of wrinkles on face images.

KW - Age classification

KW - Feature extraction

KW - Gender classification

KW - Texture

KW - Wrinkles

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DO - 10.1109/ICTer.2013.6761153

M3 - Conference Paper

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T2 - International Conference on Advances in ICT for Emerging Regions 2013

Y2 - 11 December 2013 through 15 December 2013

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