Biased face patching approach for age invariant face recognition using convolutional neural network

In recent years, a lot of interest is observed among researchers; in the domain of age invariant face recognition. The growing research interest is due to its commercial applications in many real-world scenarios. Many researchers have proposed innovative approaches to solve this problem, but still there is a significant gap. In this paper, we propose a novel technique to fill in the gap, where instead of using a whole face of a person, we use horizontal and vertical face patches. Two different feature vectors are obtained from these patches using convolutional neural networks (CNN). Then fusion of these two feature vectors is done using weighted average of features of both patches. Lastly, SVM is used as a classifier on the fused vector. Two publicly available datasets, FGNET and MORPH (album 2) are used for testing the performance of the system. This novel approach outperforms the other contemporary approaches with very good rank-1 recognition rate, on both datasets.