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Age Estimation Using OLPP Features


Affiliations
1 Department of Computer Science, Christ University, Hosur road, Bangalore, India
 

Aging face recognition poses as a key difficulty in facial recognition. It refers to identification of a person face over varied ages. It includes issues like age estimation, progression and verification. Non-availability of facial aging databases make it harder for any system to achieve good accuracy as there are no good training sets available. Age estimation when done correctly has a varied number of real life applications like age detailed vending machines, age specific access control and finding missing children. This paper implements age estimation using Park Aging Mind laboratory - Face database that contains metadata and 293 unique images of 293 individuals. Ages range from 19 to 45 with a median age of 32. Race is classified into two categories : African-American and Caucasian giving an accuracy of 98%. Sobel edge detection and Orthogonal locality preservation projection were used as the dominant features for the training and testing of age estimation. A Multi-stage binary classification using support vector machine was used to classify images into an age group thereafter predicting an individual’s age. The effectiveness of this method can be increased by using a large dataset with a wider age range.


Keywords

Face Recognition, Orthogonal Locality Preservation Projections, Age Estimation, Multi-Stage Support Vector Machine.
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  • Age Estimation Using OLPP Features

Abstract Views: 274  |  PDF Views: 7

Authors

M. S. Vaishnavi
Department of Computer Science, Christ University, Hosur road, Bangalore, India
A. Vijayalakshmi
Department of Computer Science, Christ University, Hosur road, Bangalore, India

Abstract


Aging face recognition poses as a key difficulty in facial recognition. It refers to identification of a person face over varied ages. It includes issues like age estimation, progression and verification. Non-availability of facial aging databases make it harder for any system to achieve good accuracy as there are no good training sets available. Age estimation when done correctly has a varied number of real life applications like age detailed vending machines, age specific access control and finding missing children. This paper implements age estimation using Park Aging Mind laboratory - Face database that contains metadata and 293 unique images of 293 individuals. Ages range from 19 to 45 with a median age of 32. Race is classified into two categories : African-American and Caucasian giving an accuracy of 98%. Sobel edge detection and Orthogonal locality preservation projection were used as the dominant features for the training and testing of age estimation. A Multi-stage binary classification using support vector machine was used to classify images into an age group thereafter predicting an individual’s age. The effectiveness of this method can be increased by using a large dataset with a wider age range.


Keywords


Face Recognition, Orthogonal Locality Preservation Projections, Age Estimation, Multi-Stage Support Vector Machine.

References





DOI: https://doi.org/10.13005/ojcst%2F10.01.33