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Using age information as a soft biometric trait for face image analysis
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Wang, Haoyi, Sanchez Silva, Victor, Ouyang, Wanli and Li, Chang-Tsun (2020) Using age information as a soft biometric trait for face image analysis. In: Jiang, R. and Li, C. T. and Crookes, D. and Meng, W. and Rosenberger, C., (eds.) Deep Biometrics. Unsupervised and Semi-Supervised Learning . Springer, pp. 1-20. ISBN 9783030325824
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WRAP-using-age-information-soft-biometric-trait-face-image-analysis-Li-2020.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only until 29 January 2022. Contact author directly, specifying your specific needs. - Requires a PDF viewer. Download (4Mb) |
Official URL: https://doi.org/10.1007/978-3-030-32583-1_1
Abstract
Soft biometrics refers to a group of traits that can provide some information about an individual but are inadequate for identification or recognition purposes. Age, as an important soft biometric trait, can be inferred based on the appearance of human faces. However, compared to other facial attributes like race and gender, age is rather subtle due to the underlying conditions of individuals (i.e., their upbringing environment and genes). These uncertainties make age-related face image analysis (including age estimation, age synthesis and age-invariant face recognition) still unsolved. Specifically, age estimation is concerned with inferring the specific age from human face images. Age synthesis is concerned with the rendering of face images with natural ageing or rejuvenating effects. Age-invariant face recognition involves the recognition of the identity of subjects correctly regardless of their age. Recently, thanks to the rapid development of machine learning, especially deep learning, age-related face image analysis has gained much more attention from the research community than ever before. Deep learning based models that deal with age-related face image analysis have also significantly boosted performance compared to models that only use traditional machine learning methods, such as decision trees or boost algorithms. In this chapter, we first introduce the concepts and theory behind the three main areas of age-related face image analysis and how they can be used in practical biometric applications. Then, we analyse the difficulties involved in these applications and summarise the recent progress by reviewing the state-of-the-art methods involving deep learning. Finally, we discuss the future research trends and the issues that are not addressed by existing works. We also discuss the relationship among these three areas and show how solutions within one area can help to tackle issues in the others.
| Item Type: | Book Item | ||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Science > Computer Science | ||||||||
| Library of Congress Subject Headings (LCSH): | Biometric identification, Human face recognition (Computer science), Computer vision, Image processing -- Digital techniques, Image analysis | ||||||||
| Series Name: | Unsupervised and Semi-Supervised Learning | ||||||||
| Publisher: | Springer | ||||||||
| ISBN: | 9783030325824 | ||||||||
| Book Title: | Deep Biometrics | ||||||||
| Editor: | Jiang, R. and Li, C. T. and Crookes, D. and Meng, W. and Rosenberger, C. | ||||||||
| Official Date: | 2020 | ||||||||
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| Page Range: | pp. 1-20 | ||||||||
| DOI: | 10.1007/978-3-030-32583-1_1 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| Description: | . |
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| RIOXX Funder/Project Grant: |
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