Citation:
Abstract:
This paper aims to examine the effects of aging on the efficiency of facial recognition algorithms in terms of age groups and age difference intervals. A comparative analysis of the recognition performance of two approaches is conducted for different age groups and different length time intervals between images. The first approach uses a two-dimensional discrete cosine transform (2D-DCT) and a Kernel Fisher Analysis (KFA) as description tools; classification is made using a k-NN classifier based on Euclidean distance. However, the second one is performed in two ways: first, we considered face as a single entity, then we viewed face as an independent component set. This approach makes use of Convolutional Neural Networks (CNN) for description and Support vector machines (SVM) for classification. Achieved results using the publicly accessible FG-NET face database prove that age groups influence the performance of face recognition algorithms. Also, time length lapses between images can significantly reduce the performance of face recognition.