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Age invariant face recognition: a survey on facial aging databases, techniques and effect of aging

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Abstract

Age invariant face recognition (AIFR) is highly required in many applications like law enforcement, national databases and security. Recognizing faces across aging is difficult even for humans; hence, it presents a unique challenge for computer vision systems. Face recognition under various intra-person variations such as expression, pose and occlusion has been an intensively researched field. However, age invariant face recognition still faces many challenges due to age related biological transformations in presence of the other appearance variations. In this paper, we present a comprehensive review of literature on cross age face recognition. Starting with the biological effects of aging, this paper presents a survey of techniques, effects of aging on performance analysis and facial aging databases. The published AIFR techniques are reviewed and categorized into generative, discriminative and deep learning methods on the basis of face representation and learning techniques. Analysis of the effect of aging on the performance of age-invariant face recognition system is an important dimension. Hence, such analysis is reviewed and summarized. In addition, important facial aging databases are briefly described in terms of the number of subjects and images per subject along with their age ranges. We finally present discussions on the findings, conclusions and future directions for new researchers.

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  1. Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur, India

    Manisha M. Sawant & Kishor M. Bhurchandi

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  1. Manisha M. Sawant
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Sawant, M.M., Bhurchandi, K.M. Age invariant face recognition: a survey on facial aging databases, techniques and effect of aging. Artif Intell Rev 52, 981–1008 (2019). https://doi.org/10.1007/s10462-018-9661-z

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