Age and Gender (Face) Recognition: A Brief Survey

Mascio, Tania Di; Fantozzi, Paolo; Laura, Luigi; Rughetti, Valerio

doi:10.1007/978-3-030-86618-1_11

Cited by 5 publications

(1 citation statement)

References 18 publications

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“…To the best of our knowledge, the proposed paper is the first attempt that combines various face analysis tasks in a single comprehensive review article. Survey papers for each of these tasks (race, age, gender, expression, and HPE) are individually available [54][55][56]; however, there is an intense need for a single paper combining these demographic components. We specifically focus on the works that have been published in the last 10 years.…”

Section: Contributions and Paper Organizationmentioning

confidence: 99%

Face Image Analysis Using Machine Learning: A Survey on Recent Trends and Applications

Ṣiddiqi

Khan

et al. 2022

Electronics

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Human face image analysis using machine learning is an important element in computer vision. The human face image conveys information such as age, gender, identity, emotion, race, and attractiveness to both human and computer systems. Over the last ten years, face analysis methods using machine learning have received immense attention due to their diverse applications in various tasks. Although several methods have been reported in the last ten years, face image analysis still represents a complicated challenge, particularly for images obtained from ’in the wild’ conditions. This survey paper presents a comprehensive review focusing on methods in both controlled and uncontrolled conditions. Our work illustrates both merits and demerits of each method previously proposed, starting from seminal works on face image analysis and ending with the latest ideas exploiting deep learning frameworks. We show a comparison of the performance of the previous methods on standard datasets and also present some promising future directions on the topic.

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Section: Contributions and Paper Organizationmentioning

confidence: 99%