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A Development of Efficient Algorithm for Age Detection from Face Images


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1 Department of Computer Science, Gyan Ganga Institute of Technology & Science, Jabalpur, India
     

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Biometrical system have variety of applications that helps in fields of identification, verification and security etc. face image have many characteristics. Face images shows different aspects of human expressions such as gender, age, emotions, and facial expressions, face color and skin texture etc. Such type of image based face is recognized by different methods and algorithms are applied on image and is widely used in some security purpose, registration process and also identification process or verification processes. Automatic age detection is one of the main issues in pattern recognition which shows the age of human according to her/his facial expressions. This paper presents two feature extraction and for age classification methods. Proposed methods are directional 2DPCA and wavelet methods for feature extraction and for age classification using k-nearest neighbor and multiclass SVM methods.


Keywords

Biometrics, Feature Extraction, Age Classification, FG-NET Database, Two Directional 2DPCA, Wavelet DWT, k-NN and Multiclass SVM.
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  • A Development of Efficient Algorithm for Age Detection from Face Images

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Authors

Krati Bakshi
Department of Computer Science, Gyan Ganga Institute of Technology & Science, Jabalpur, India
Preeti Rai
Department of Computer Science, Gyan Ganga Institute of Technology & Science, Jabalpur, India

Abstract


Biometrical system have variety of applications that helps in fields of identification, verification and security etc. face image have many characteristics. Face images shows different aspects of human expressions such as gender, age, emotions, and facial expressions, face color and skin texture etc. Such type of image based face is recognized by different methods and algorithms are applied on image and is widely used in some security purpose, registration process and also identification process or verification processes. Automatic age detection is one of the main issues in pattern recognition which shows the age of human according to her/his facial expressions. This paper presents two feature extraction and for age classification methods. Proposed methods are directional 2DPCA and wavelet methods for feature extraction and for age classification using k-nearest neighbor and multiclass SVM methods.


Keywords


Biometrics, Feature Extraction, Age Classification, FG-NET Database, Two Directional 2DPCA, Wavelet DWT, k-NN and Multiclass SVM.

References