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Article

Human Attribute Recognition— A Comprehensive Survey

1
IT: Instituto de Telecomunicações, University of Beira Interior, 6201-001 Covilhã, Portugal
2
Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran
3
Faculty of Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
4
Faculty of Computer Science, University of Beira Interior, 6201-001 Covilhã, Portugal
5
TomiWorld, 3500-106 Viseu, Portugal
*
Authors to whom correspondence should be addressed.
Current address: SOCIA Lab., Faculty of Computer Science, University of Beira Interior, Portugal.
Appl. Sci. 2020, 10(16), 5608; https://doi.org/10.3390/app10165608
Received: 2 July 2020 / Revised: 24 July 2020 / Accepted: 8 August 2020 / Published: 13 August 2020
(This article belongs to the Section Computing and Artificial Intelligence)
Human Attribute Recognition (HAR) is a highly active research field in computer vision and pattern recognition domains with various applications such as surveillance or fashion. Several approaches have been proposed to tackle the particular challenges in HAR. However, these approaches have dramatically changed over the last decade, mainly due to the improvements brought by deep learning solutions. To provide insights for future algorithm design and dataset collections, in this survey, (1) we provide an in-depth analysis of existing HAR techniques, concerning the advances proposed to address the HAR’s main challenges; (2) we provide a comprehensive discussion over the publicly available datasets for the development and evaluation of novel HAR approaches; (3) we outline the applications and typical evaluation metrics used in the HAR context. View Full-Text
Keywords: human attribute recognition; imbalanced learning; pedestrian recognition; privacy concerns; clothing attributes; soft biometrics; appearance-based learning; deep learning human attribute recognition; imbalanced learning; pedestrian recognition; privacy concerns; clothing attributes; soft biometrics; appearance-based learning; deep learning
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MDPI and ACS Style

Yaghoubi, E.; Khezeli, F.; Borza, D.; Kumar, S.A.; Neves, J.; Proença, H. Human Attribute Recognition— A Comprehensive Survey. Appl. Sci. 2020, 10, 5608. https://doi.org/10.3390/app10165608

AMA Style

Yaghoubi E, Khezeli F, Borza D, Kumar SA, Neves J, Proença H. Human Attribute Recognition— A Comprehensive Survey. Applied Sciences. 2020; 10(16):5608. https://doi.org/10.3390/app10165608

Chicago/Turabian Style

Yaghoubi, Ehsan, Farhad Khezeli, Diana Borza, SV A. Kumar, João Neves, and Hugo Proença. 2020. "Human Attribute Recognition— A Comprehensive Survey" Applied Sciences 10, no. 16: 5608. https://doi.org/10.3390/app10165608

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