Digital Image Processing

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Discriminative Approach for Age Invariant Face Recognition Using CNN and SVM


Affiliations
1 Department of Computer Science and Engineering, Global Academy of Technology, Bengaluru-560098, Karnataka, India
2 Department of Computer Science and Engineering, Global Academy of Technology, Bengaluru-560098, Karnataka, India
     

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Face Recognition across age has been a challenging and popular task in the field of Face Recognition. Although many researchers are contributing to the area, there’s a significant gap to fill in. Data collections and feature extraction are the most challenging tasks to be tackled in Age Invariant Face Recognition (AIFR). Feature extraction could be achieved using the Convolutional Neural Networks (CNN) and for classification. But due to unavailability of large, paired datasets, opting for a Deep Neural Network architecture for this problem statement would not result in a good accuracy. Therefore, we propose a CNN-SVM architecture where CNN extracts important features from an image, which is then used by SVM for final classification.

Keywords

Age Invariant Face Recognition (AIFR), CNN, SVM, Tensor Flow, Face Detection.
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  • Zhifeng L., et al., “A Discriminative Model for Age Invariant Face Recognition,” IEEE Transactions on Information Forensics and Security, vol. 6, no. 3, pp. 1028-1037, 2011.

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  • Discriminative Approach for Age Invariant Face Recognition Using CNN and SVM

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Authors

HB Chaitanya Bharadwaj
Department of Computer Science and Engineering, Global Academy of Technology, Bengaluru-560098, Karnataka, India
BS. Avinash
Department of Computer Science and Engineering, Global Academy of Technology, Bengaluru-560098, Karnataka, India
MC. Chethan
Department of Computer Science and Engineering, Global Academy of Technology, Bengaluru-560098, Karnataka, India
G. Darshan
Department of Computer Science and Engineering, Global Academy of Technology, Bengaluru-560098, Karnataka, India
R. Kanagavalli
Department of Computer Science and Engineering, Global Academy of Technology, Bengaluru-560098, Karnataka, India

Abstract


Face Recognition across age has been a challenging and popular task in the field of Face Recognition. Although many researchers are contributing to the area, there’s a significant gap to fill in. Data collections and feature extraction are the most challenging tasks to be tackled in Age Invariant Face Recognition (AIFR). Feature extraction could be achieved using the Convolutional Neural Networks (CNN) and for classification. But due to unavailability of large, paired datasets, opting for a Deep Neural Network architecture for this problem statement would not result in a good accuracy. Therefore, we propose a CNN-SVM architecture where CNN extracts important features from an image, which is then used by SVM for final classification.

Keywords


Age Invariant Face Recognition (AIFR), CNN, SVM, Tensor Flow, Face Detection.

References