Meng, H;
Bianchi-Berthouz, N;
(2013)
Affective state level recognition in naturalistic facial and vocal expressions.
IEEE Transactions on Cybernetics
, 44
(3)
315- 328.
10.1109/TCYB.2013.2253768.
![[thumbnail of 06507321.pdf]](https://discovery-pp.ucl.ac.uk/style/images/fileicons/application_pdf.png) Preview |
PDF
06507321.pdf Download (1MB) |
Abstract
Naturalistic affective expressions change at a rate much slower than the typical rate at which video or audio is recorded. This increases the probability that consecutive recorded instants of expressions represent the same affective content. In this paper, we exploit such a relationship to improve the recognition performance of continuous naturalistic affective expressions. Using datasets of naturalistic affective expressions (AVEC 2011 audio and video dataset, PAINFUL video dataset) continuously labeled over time and over different dimensions, we analyze the transitions between levels of those dimensions (e.g., transitions in pain intensity level). We use an information theory approach to show that the transitions occur very slowly and hence suggest modeling them as first-order Markov models. The dimension levels are considered to be the hidden states in the Hidden Markov Model (HMM) framework. Their discrete transition and emission matrices are trained by using the labels provided with the training set. The recognition problem is converted into a best path-finding problem to obtain the best hidden states sequence in HMMs. This is a key difference from previous use of HMMs as classifiers. Modeling of the transitions between dimension levels is integrated in a multistage approach, where the first level performs a mapping between the affective expression features and a soft decision value (e.g., an affective dimension level), and further classification stages are modeled as HMMs that refine that mapping by taking into account the temporal relationships between the output decision labels. The experimental results for each of the unimodal datasets show overall performance to be significantly above that of a standard classification system that does not take into account temporal relationships. In particular, the results on the AVEC 2011 audio dataset outperform all other systems presented at the international competition.
| Type: | Article |
|---|---|
| Title: | Affective state level recognition in naturalistic facial and vocal expressions |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TCYB.2013.2253768 |
| Publisher version: | http://dx.doi.org/10.1109/TCYB.2013.2253768 |
| Language: | English |
| Additional information: | © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Keywords: | Emotion recognition, Affective dimensions, Automatic emotion recognition, Facial expressions, Vocal expressions, HMM |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > UCL Interaction Centre |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/1376761 |
Archive Staff Only
 |
View Item |
