How Does Aging Affect Facial Components?

Abstract

Abstract. There is growing interest in achieving age invariant face recognition due to its wide applications in law enforcement. The challenge lies in that face aging is quite a complicated process, which involves both intrinsic and extrinsic factors. Face aging also influences individual facial components (such as the mouth, eyes, and nose) differently. We propose a component based method for age invariant face recognition. Facial components are automatically localized based on landmarks detected using an Active Shape Model. Multi-scale local binary pattern and scale-invariant feature transform features are then extracted from each component, followed by random subspace linear discriminant analysis for classification. With a component based representation, we study how aging influences individual facial components on two large aging databases (MORPH Album2 and PCSO). Per component performance analysis shows that the nose is the most stable component during face aging. Age invariant recognition exploiting demographics shows that face aging has more influence on females than males. Overall, recognition performance on the two databases shows that the proposed component based approach is more robust to large time lapses than FaceVACS, a leading commercial face matcher.

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“…This is because, face images generated using GANs are pseudofaces of lower quality containing the inferred content. The pseudofaces result in poor matching accuracy due to poor quality [36]. This results in higher EER for GAN based generated face images.…”