Paper Details

In current research, face recognition technology is being used to improve human efficiency when recognizingfaces, is one of the fastest growing fields in the biometric industry. The challenges in developing age invariant facerecognition are large intra-subject variations and large inter-user similarity. The main intra-subject variations are(pose, illumination, expression, and aging) commonly encountered in face recognition. In this paper, feature extractionbased age invariant face recognition framework is modelled. In this system first component or features are extractedusing two local feature extraction SIFT (scale invariant feature extraction scheme) and MLBP (multi-scale local binarypattern) methods. This feature vector is then classified using artificial neural network. These results are normalized andusing score sum fusion rule recognition in performs. Evaluation of this system is done by different measuring parametersto check the robustness of the proposed system. Experimental results shows that the accuracy is increased up to 53%compared to existing approach.

M. J. Raval, Shankar Parmar, “Age Invariant Face Recognition Using Artificial Neural Network”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 2, Issue 6, pp. 121-128, June 2015.