A comparative study of age-invariant face recognition with different feature representations

Age invariant face recognition is an important yet less investigated problem in the face recognition community. In this paper, we empirically evaluate state-of-the-art facial feature representations for age-invariant face recognition. Three representative features including local binary pattern (LBP), Gabor wavelets and gradient orientation pyramid (GOP) were applied, followed by a principal component analysis (PCA) to reduce the dimensions of the extracted features. Experimental results on the MORPH database, one of the largest publicly available face dataset containing thousands of longitudinal images are presented. Experimental results show that Gabor wavelets feature with five scales and eight orientations is the optimal feature representation method for age-invariant face recognition.

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