Affective gait recognition and baseline evaluation from real world samples

Kellokumpu, Vili; Särkiniemi, Markus; Zhao, Guoying (2017-03-15)
 
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https://doi.org/10.1007/978-3-319-54407-6_38

Kellokumpu, Vili
Särkiniemi, Markus
Zhao, Guoying
Springer Nature
15.03.2017

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

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© 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.
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doi:https://doi.org/10.1007/978-3-319-54407-6_38
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https://urn.fi/URN:NBN:fi-fe2019041111887
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Abstract

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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