University of Oulu

Kellokumpu V., Särkiniemi M., Zhao G. (2017) Affective Gait Recognition and Baseline Evaluation from Real World Samples. In: Chen CS., Lu J., Ma KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science, vol 10116. Springer, Cham

Affective gait recognition and baseline evaluation from real world samples

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Author: Kellokumpu, Vili1; Särkiniemi, Markus1; Zhao, Guoying1
Organizations: 1Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2.8 MB)
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Language: English
Published: Springer Nature, 2017
Publish Date: 2019-04-11


Over the years a lot of research efforts have been put into recognizing human emotions from facial expressions. However, in many scenarios access to suitable face data is difficult, and therefore there is a need for methodology that can be used when people are observed from a distance. A potential modality for this is human gait. Early attempts to recognize human emotion from gait have been limited to acted data. Furthermore, in these approaches the data has been captured in controlled settings. This paper presents the first experiments for automated affective gait recognition using non acted real world samples. A database of 96 subjects affected by positive or negative feedback is collected and two baseline methods are used to recognize the affective state of a person. The baseline results are promising and encourage further study in this domain.

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Series: Lecture notes in computer science
ISSN: 0302-9743
ISSN-E: 1611-3349
ISSN-L: 0302-9743
ISBN: 978-3-319-54407-6
ISBN Print: 978-3-319-54406-9
Pages: 567 - 575
DOI: 10.1007/978-3-319-54407-6_38
Host publication: Computer Vision – ACCV 2016 Workshops : ACCV 2016 International Workshops, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part I
Host publication editor: Chen, Chu-Song
Lu, Jiwen
Ma, Kai-Kuang
Conference: Asian Conference on Computer Vision
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Funding: This work was sponsored by the Academy of Finland, Infotech Oulu and Nokia Visiting Professor grant.
Copyright information: © Springer International Publishing AG 2017. This is a post-peer-review, pre-copyedit version of an article published in Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science, vol 10116. The final authenticated version is available online at: