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
|Author:||Kellokumpu, Vili1; Särkiniemi, Markus1; Zhao, Guoying1|
1Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 2.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019041111887
|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.
Lecture notes in computer science
|Pages:||567 - 575|
Computer Vision – ACCV 2016 Workshops : ACCV 2016 International Workshops, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part I
|Host publication editor:||
Asian Conference on Computer Vision
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
This work was sponsored by the Academy of Finland, Infotech Oulu and Nokia Visiting Professor grant.
© 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: http://dx.doi.org/978-3-319-54407-6_38.