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Neural Network based Age and Gender Classification for Facial Images

Authors:

Thakshila R Kalansuriya ,

University of Colombo School of Computing, No. 35, Reid Avenue, Colombo 07, LK
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Anuja T Dharmaratne

University of Colombo School of Computing, No. 35, Reid Avenue, Colombo 07, LK
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Abstract

Automatic face identification and verification from facial images attain good accuracy with large sets of training data while face attribute recognition from facial images still remain challengeable. Hence introducing an efficient and accurate facial image classification based on facial attributes is an important task. This paper proposes a methodology for automatic age and gender classification based on feature extraction from facial images. In contrast to the other mechanisms proposed in the literature, the main concern of this methodology is the use of biometric feature variation of male and female for the classification. It uses two types of features namely, primary and secondary features and it includes three main iterations: Preprocessing, Feature extraction and Classification. This study has been carried out using facial images of age range 8-60 years consisting of both gender types and the age classification has been done according to predefined age ranges. Proposed 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. This study has been evaluated and tested on both foreign and Asian face images in both gender types and the four age categories used.

International Journal on Advances in ICT for Emerging Regions 2014 07(02)

DOI: http://dx.doi.org/10.4038/icter.v7i2.7154


How to Cite: Kalansuriya, T.R. & Dharmaratne, A.T., (2015). Neural Network based Age and Gender Classification for Facial Images. International Journal on Advances in ICT for Emerging Regions (ICTer). 7(2), pp.1–10. DOI: http://doi.org/10.4038/icter.v7i2.7154
Published on 20 Apr 2015.
Peer Reviewed

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