i-manager Publications

Face Recognition: Dealing With Under-Sampled Data and Image Variation Problems

Sri Harsha*, S. Phani Kumar**

* PG Scholar, Department of Computer Science and Engineering, GITAM School of Technology, Hyderabad, India.

** Professor and Head, Department of Computer Science and Engineering, GITAM School of Technology, Hyderabad, India.

Periodicity:October - December'2017
DOI : https://doi.org/10.26634/jip.4.4.14161

Abstract

Accuracy is one of the major concerns for any face recognition algorithm. A good preprocessing technique can increase the accuracy of a face recognition system. For this purpose, the authors present a preprocessing method for face recognition, which is helpful for training under-sampled images. For this purpose, different preprocessing techniques are taken and those methods are used during training and the resultant images are added back to the training set. Each individual preprocessing technique is helpful to tackle different conditions like lighting, disguise, etc. The training will be done using a deep convolutional neural network and a Voting Classifier. Further the results of the existing preprocessing methods are compared with the proposed model on LFW dataset. They have also used a technique to increase the accuracy of the system.

Keywords

Face Recognition, Under-Sampled Data, Convolutional Neural Network, Voting Classifier.

How to Cite this Article?

Harsha, A.S. and Kumar, S.P. (2017). Face Recognition: Dealing With Under-Sampled Data and Image Variation Problems. i-manager’s Journal on Image Processing, 4(4), 22-30. https://doi.org/10.26634/jip.4.4.14161

References

[1]. Ahmadi, A. H., & Jamzad, M. (2011). Face recognition under varying illumination based on histogram equalized processed images. In Image and Signal Processing and Analysis (ISPA), 2011 ^7th International Symposium on (pp. 349-354). IEEE.

[2]. Ahonen, T., Hadid, A., & Pietikäinen, M. (2004, May). Face recognition with local binary patterns. In European Conference on Computer Vision (pp. 469-481). Springer, Berlin, Heidelberg.

[3]. Amos, B., Ludwiczuk, B., & Satyanarayanan, M. (2016). Openface: A general-purpose face recognition librar y with mobile applications. CMU School of Computer Science (pp. 1-18).

[4]. Chande, H. D., & Shah, Z. H. (2012). Illumination Invariant Face Recognition System. International Journal of Computer Science and Information Technologies, 3(3), 4010-4014.

[5]. Deng, W., Hu, J., & Guo, J. (2012). Extended SRC: Undersampled face recognition via intraclass variant dictionary. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(9), 1864-1870.

[6]. Dharavath, K., Talukdar, F. A., & Laskar, R. H. (2014). Improving face recognition rate with image preprocessing. Indian Journal of Science and Technology, 7(8), 1170-1175.

[7]. Han, H., Shan, S., Chen, X., & Gao, W. (2013). A comparative study on illumination preprocessing in face recognition. Pattern Recognition, 46(6), 1691-1699.

[8]. Huang, G. B., Mattar, M., Berg, T., & Learned-Miller, E. (2008). Labeled faces in the wild: A database for studying face recognition in unconstrained environments. In Workshop on Faces in 'Real-Life'Images: Detection, Alignment, and Recognition.

[9]. Jafri, R., & Arabnia, H. R. (2009). A survey of face recognition techniques. Journal of Information Processing Systems, 5(2), 41-68.

[10]. Karthikeyan, V., Vijayalakshmi, K., & Jeyakumar, P. (2012). An Efficient Method for Face Recognition System in various assorted conditions. Biometrics and Bioinformatics, 4(12), 543-547.

[11]. Lawrence, S., Giles, C. L., Tsoi, A. C., & Back, A. D. (1997). Face recognition: A convolutional neural-network approach. IEEE Transactions on Neural Networks, 8(1), 98- 113.

[12]. Tensor Face. (2018). Retrieved from https://github. com/AKSHAYUBHAT/TensorFace/blob/master/openface/ models/dlib/shape_predictor_68_face_landmarks.dat

[13]. Wei, C. P., & Wang, Y. C. F. (2013, July). Learning auxiliary dictionaries for undersampled face recognition. In Multimedia and Expo (ICME), 2013 IEEE International Conference on (pp. 1-6). IEEE.

[14]. Wei, C. P., & Wang, Y. C. F. (2015). Undersampled face recognition via robust auxiliary dictionary learning. IEEE Transactions on Image Processing, 24(6), 1722-1734.

[15]. Zhang, H. (2011). Image Preprocessing Methods in Face Recognition. Symposium on Photonics and Optoelectronics (SOPO), (pp. 1-4.

Face Recognition: Dealing With Under-Sampled Data and Image Variation Problems

Abstract

Keywords

How to Cite this Article?

References

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