Feature extraction based face recognition, gender and age classification


Downloads per month over past year

Ramesha, K. and Raja, K B . and Venugopal, K .R. and Patnaik, L.M. (2010) Feature extraction based face recognition, gender and age classification. International Journal on Computer Science and Engineering,, 02. pp. 14-23. ISSN 0975-3397


Download (742kB) | Preview


The face recognition system with large sets of training sets for personal identification normally attains good accuracy. In this paper, we proposed Feature Extraction based Face Recognition, Gender and Age Classification (FEBFRGAC) algorithm with only small training sets and it yields good results even with one image per person. This process involves three stages: Pre-processing, Feature Extraction and Classification. The geometric features of facial images like eyes, nose, mouth etc. are located by using Canny edge operator and face recognition is performed. Based on the texture and shape information gender and age classification is done using Posteriori Class Probability and Artificial Neural Network respectively. It is observed that the face recognition is 100%, the gender and age classification is around 98% and 94% respectively.

Item Type: Article
Uncontrolled Keywords: Age Classification, Artificial Neural Networks, Face Recognition, Gender Classification, Shape and Texture Transformation, Wrinkle Texture
Subjects: Faculty of Engineering > Computer Science & Information Science Engineering
Divisions: University Visvesvarayya College of Engineering > Department of Computer Science and Information Science Engineering
Depositing User: Ms Laxmi Kamble
Date Deposited: 15 Sep 2021 06:08
Last Modified: 15 Sep 2021 06:08
URI: http://eprints-bangaloreuniversity.in/id/eprint/9492

Actions (login required)

View Item View Item