Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Human Attribute Recognition: A Comprehensive Survey

Version 1 : Received: 2 July 2020 / Approved: 5 July 2020 / Online: 5 July 2020 (08:21:00 CEST)

A peer-reviewed article of this Preprint also exists.

Yaghoubi, E.; Khezeli, F.; Borza, D.; Kumar, S.A.; Neves, J.; Proença, H. Human Attribute Recognition— A Comprehensive Survey. Appl. Sci. 2020, 10, 5608. Yaghoubi, E.; Khezeli, F.; Borza, D.; Kumar, S.A.; Neves, J.; Proença, H. Human Attribute Recognition— A Comprehensive Survey. Appl. Sci. 2020, 10, 5608.

Journal reference: Appl. Sci. 2020, 10, 5608
DOI: 10.3390/app10165608

Abstract

Over the last decade, the field of Human Attribute Recognition (HAR) has dramatically changed, mainly due to the improvements brought by deep learning solutions. This survey reviews the progress obtained in HAR, considering the transition from the traditional hand-crafted to deep-learning approaches. The most relevant works on the field are analyzed concerning the advances proposed to address the HAR's typical challenges. Furthermore, we outline the applications and typical evaluation metrics used in the HAR context. Finally, we provide a comprehensive review of the publicly available datasets for the development and evaluation of novel HAR approaches.

Subject Areas

Human Attribute Recognition; Imbalanced Learning; Pedestrian Recognition; Privacy Concerns; Clothing Attributes; Soft Biometrics; Appearance-Based Learning; Deep Learning

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