Soft-Ranking Label Encoding for Robust Facial Age Estimation

• Published in: IEEE access Vol. 8; pp. 134209 - 134218
• Main Authors: Zeng, Xusheng, Huang, Junyang, Ding, Changxing
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Age; age encoding; Age estimation; Aging; Chronology; Correlation; Encoding; Estimation; Face; facial attribute analysis; Image coding; Ranking; Training
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3010815

Automatic facial age estimation can be used in a wide range of real-world applications. However, this process is challenging due to the randomness and slowness of the aging process. Accordingly, in this paper, we propose a novel method aimed at overcoming the challenges associated with facial age estimation. First, we propose a novel age encoding method, referred to as 'Soft-ranking', which encodes two important properties of facial age, <inline-formula> <tex-math notation="LaTeX">{i.e.} </tex-math></inline-formula>, the ordinal property and the correlation between adjacent ages. Therefore, Soft-ranking provides a richer supervision signal for training deep models. Moreover, we carefully analyze existing evaluation protocols for age estimation, finding that the overlap in identity between the training and testing sets affects the relative performance of different age encoding methods. Moreover, we achieve state-of-the-art performance on four most popular age databases, <inline-formula> <tex-math notation="LaTeX">{i.e.} </tex-math></inline-formula>, Morph II, AgeDB, CLAP2015, and CLAP2016.