ICTACT Journal on Image and Video Processing

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Age Invariant Face Recognition Using Quadratic Support Vector Machine – Principal Component Analysis


Affiliations
1 Department of Electronics and Communication Engineering, Deenbandhu Chhotu Ram University of Science and Technology, India
     

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Age Invariant Face Recognition (AIFR) is a new research area in the domain of face recognition that lately received a lot of interest due to its tremendous potential with relevant applications in real-world. It is very challenging research problem to extract robust features describing aging facial information, usually when broad age difference between face images are perceived. In this paper, by using the quadratic support vector machine and principal component analysis technique of feature selection that is robust to aging we address this challenge and achieved the highest accuracy as compared to other methods such as K-Neighbor Neural Network (KNN), Probabilistic Neural Network (PNN), Backpropagation Neural Network (BPNN). FGNET aging dataset is used for the implementation of proposed method which includes a total of 82 separate subjects comprising 1002 images and each subject includes 6-18 images per subject obtained primarily from scanning photographs of subjects between the age ranging from newborn to 69-year-old subjects. From the images, LBP, Gabor and shape features have been extracted and PCA is used as a method of feature reduction. Four stages have been explored in this work: (a) Face recognition (b) Identify the gender of the object (c) Identify the age of the subject using the backpropagation neural network (BPNN) (d) Age invariant face recognition using KNN, PNN, NN and QSVM-PCA. The highest accuracy is achieved using the Quadratic Support Vector Machine – Principal Component Analysis (QSVM-PCA) classifier.

Keywords

Age Invariant Face Recognition, Facial Aging, QSVM, Databases.
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  • U. Park, Y. Tong and A.K. Jain, “Age-Invariant Face Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, No. 5, pp. 947-954, 2010.

  • Z. Li, U. Park and A.K. Jain, “A Discriminative Model for Age Invariant Face Recognition”, IEEE Transactions on Information Forensics and Security, Vol. 6, No. 3, pp. 1028-1037, 2011.

  • D. Gong, Z. Li, D. Lin, J. Liu and X. Tang, “Hidden Factor Analysis for Age Invariant Face Recognition”, Proceedings of International Conference on Computer Vision, pp. 2872-2879, 2013.

  • D. Gong, Z. Li, D. Tao, J. Liu and X. Li, “A Maximum Entropy Feature Descriptor for Age Invariant Face Recognition”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 5289-5297, 2015.

  • C. Xu, Q. Liu and M. Ye, “Age Invariant Face Recognition and Retrieval by Coupled Auto-Encoder Networks”, Neurocomputing, Vol. 222, pp. 62-71, 2017.

  • Y. Wen, Z. Li and Y. Qiao, “Latent Factor Guided Convolutional Neural Networks for Age-invariant Face Recognition”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 4893-4901, 2016.

  • M. Sharif, F. Naz, M. Yasmin, M. A. Shahid and A. Rehman, “Face Recognition: A Survey”, Journal of Engineering Science and Technology Review, Vol. 10, No. 2, pp. 1-13, 2017.

  • M.M. Sawant and K.M. Bhurchandi, “Age Invariant Face Recognition: A Survey on Facial Aging Databases, Techniques and Effect of Aging”, Artificial Intelligence Review, Vol. 52, pp. 981-1008, 2019.

  • S. Moschoglou, A. Papaioannou, C. Sagonas, J. Deng, I. Kotsia and S. Zafeiriou, “AgeDB: The First Manually Collected, in-the-Wild Age Database”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 51-59, 2017.

  • T. Cootes, “FGNET Face and Gesture Recognition Database”, Face and Gesture Recognition Working Group, Available at: http://www-prima.inrialpes.fr/FGnet/, Accessed at 2016.

  • A.K. Jain and S.Z. Li, “Handbook of Face Recognition”, Springer, 2011.

  • M. Chihaoui, A. Elkefi, W. Bellil and C. Ben Amar, “A Survey of 2D Face Recognition Techniques”, Computers, Vol. 5, No. 4, pp. 1-21, 2016.

  • A.K. Agarwal and Y.N. Singh, “Evaluation of Face Recognition Methods in Unconstrained Environments”, Procedia Computer Science, Vol. 48, pp. 644-651, 2015.

  • K.Y. Chang and C.S. Chen, “A Learning Framework for Age Rank Estimation Based on Face Images with Scattering Transform”, IEEE Transactions on Image Processing, Vol. 24, No. 3, pp. 785-798, 2015.

  • J. Lu, V.E. Liong and J. Zhou, “Cost-sensitive Local Binary Feature Learning for Facial Age Estimation”, IEEE Transactions on Image Processing, Vol. 24, No. 12, pp. 5356-5368, 2015.

  • I. Huerta, C. Fernández, C. Segura, J. Hernando and A. Prati, “A Deep Analysis on Age Estimation”, Pattern Recognition Letters, Vol. 68, pp. 239-249, 2015.

  • Y.H. Kwon and N. Da Vitoria Lobo, “Age Classification from Facial Images”, Computer Vision and Image Understanding, Vol. 74, No. 1, pp. 1-21, 1999.

  • G. Guo, Y. Fu, C.R. Dyer and T.S. Huang, “Image-based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression”, IEEE Transactions on Image Processing, Vol. 17, No. 7, pp. 1178-1188, 2008.

  • Y. Fu and T. S. Huang, “Human Age Estimation with Regression on Discriminative Aging Manifold”, IEEE Transactions on Multimedia, Vol. 10, No. 4, pp. 578-584, 2008.

  • S. Sahni and S. Saxena, “Survey: Techniques for Aging Problems in Face Recognition”, MIT International Journal of Computer Science and Information Technology, Vol. 4, No. 2, pp. 82-88, 2014.


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  • Age Invariant Face Recognition Using Quadratic Support Vector Machine – Principal Component Analysis

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Authors

Deepika
Department of Electronics and Communication Engineering, Deenbandhu Chhotu Ram University of Science and Technology, India
Priyanka
Department of Electronics and Communication Engineering, Deenbandhu Chhotu Ram University of Science and Technology, India

Abstract


Age Invariant Face Recognition (AIFR) is a new research area in the domain of face recognition that lately received a lot of interest due to its tremendous potential with relevant applications in real-world. It is very challenging research problem to extract robust features describing aging facial information, usually when broad age difference between face images are perceived. In this paper, by using the quadratic support vector machine and principal component analysis technique of feature selection that is robust to aging we address this challenge and achieved the highest accuracy as compared to other methods such as K-Neighbor Neural Network (KNN), Probabilistic Neural Network (PNN), Backpropagation Neural Network (BPNN). FGNET aging dataset is used for the implementation of proposed method which includes a total of 82 separate subjects comprising 1002 images and each subject includes 6-18 images per subject obtained primarily from scanning photographs of subjects between the age ranging from newborn to 69-year-old subjects. From the images, LBP, Gabor and shape features have been extracted and PCA is used as a method of feature reduction. Four stages have been explored in this work: (a) Face recognition (b) Identify the gender of the object (c) Identify the age of the subject using the backpropagation neural network (BPNN) (d) Age invariant face recognition using KNN, PNN, NN and QSVM-PCA. The highest accuracy is achieved using the Quadratic Support Vector Machine – Principal Component Analysis (QSVM-PCA) classifier.

Keywords


Age Invariant Face Recognition, Facial Aging, QSVM, Databases.

References