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Face Image Analysis Using Machine Learning: A Survey on Recent Trends and Applications. ELECTRONICS 2022. [DOI: 10.3390/electronics11081210] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
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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Similarity mapping for robust face recognition via a single training sample per person. Pattern Recognit Lett 2019. [DOI: 10.1016/j.patrec.2019.10.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Nixon MS, Guo BH, Stevenage SV, Jaha ES, Almudhahka N, Martinho-Corbishley D. Towards automated eyewitness descriptions: describing the face, body and clothing for recognition. VISUAL COGNITION 2017. [DOI: 10.1080/13506285.2016.1266426] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Mark S. Nixon
- Department of Electronics and Computer Science, University of Southampton, Southampton, UK
| | - Bingchen H. Guo
- Department of Electronics and Computer Science, University of Southampton, Southampton, UK
| | | | - Emad S. Jaha
- Department of Electronics and Computer Science, University of Southampton, Southampton, UK
| | - Nawaf Almudhahka
- Department of Electronics and Computer Science, University of Southampton, Southampton, UK
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Nixon MS, Correia PL, Nasrollahi K, Moeslund TB, Hadid A, Tistarelli M. On soft biometrics. Pattern Recognit Lett 2015. [DOI: 10.1016/j.patrec.2015.08.006] [Citation(s) in RCA: 73] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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