Distinctive feature representations and classifiers for age invariant face recognition - a survey

Abstract

Face recognition across ages is very challenging area for research. It is highly affected with many uncontrolled factors like expressions, lightning, head position and aging. Though many researchers contributed their studies to solve this problem still it is unsolved. Facial feature representations and classifiers play an important role in age invariant face recognition. Thus, in this paper, we have presented a complete survey on distinctive feature representations along with classifiers useful in this area. The study includes features like LBP and its various modifications, Gabor wavelets, gradient orientation pyramid (GOP), α-shape and PC-based features. As classifiers play an important role to recognise face images across ages, some of the classifiers like support vector machines, neural networks, convolutional neural networks, etc. are also discussed. The comparative analysis of distinctive age invariant face recognition models is also presented in this paper. Two very widely and publically available datasets in this area, FGNET and MORPH are also discussed in details and compared.