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

Volume 4 Issue 4 October - December 2017

Research Paper

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.

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

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.