Gender Classification from Gait Energy and Posture Images Using Multi-stage Network

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationPattern Recognition
Subtitle of host publication7th Asian Conference, ACPR 2023, Kitakyushu, Japan, November 5–8, 2023, Proceedings, Part III
EditorsHuimin Lu, Michael Blumenstein, Sung-Bae Cho, Cheng-Lin Liu, Yasushi Yagi, Tohru Kamiya
PublisherSpringer
Pages162-173
ISBN (electronic)978-3-031-47665-5
ISBN (print)978-3-031-47664-8
Publication statusPublished - 2023

Publication series

NameLecture Notes in Computer Science
Volume14408
ISSN (Print)0302-9743
ISSN (electronic)1611-3349

Conference

Title7th Asian Conference on Pattern Recognition (ACPR 2023)
PlaceJapan
CityKitakyushu
Period5 - 8 November 2023

Abstract

Gait-based gender classification from an image sequence captured at a distance from human subjects can provide valuation information for video surveillance. One common approach is to adopt machine learning for the prediction of the gender class. Algorithms perform gender classification based on spatio-temporal feature, e.g., Gait Energy Image (GEI), extracted from the video. Although GEI can concisely characterize the movements over a gait cycle, it has some limitations. For instance, GEI lacks photometric information and does not exhibit a clear posture of the subject. To improve gender classification, we think that more features must be utilized. In this paper, we propose a gender classification framework that exploits not only the GEI, but also the characteristic poses of the walking cycle. The proposed framework is a multi-stream and multi-stage network that is capable of gradually learning the gait features from multiple modality inputs acquired in multiple views. The extracted features are fused and input to the classifier which is trained with ensemble learning. We evaluate and compare the performance of our proposed model with a variety of gait-based gender classification methods on two benchmark datasets. Through thorough experimentations, we demonstrate that our proposed model achieves higher gender classification accuracy than the methods that utilize only either GEI, or posture image. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Research Area(s)

  • ensemble learning, gait classification, gait energy image, walking cycle

Citation Format(s)

Gender Classification from Gait Energy and Posture Images Using Multi-stage Network. / Leung, Tak-Man; Chan, Kwok-Leung.
Pattern Recognition: 7th Asian Conference, ACPR 2023, Kitakyushu, Japan, November 5–8, 2023, Proceedings, Part III. ed. / Huimin Lu; Michael Blumenstein; Sung-Bae Cho; Cheng-Lin Liu; Yasushi Yagi; Tohru Kamiya. Springer, 2023. p. 162-173 (Lecture Notes in Computer Science; Vol. 14408).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review