3 research outputs found
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Automatic affective dimension recognition from naturalistic facial expressions based on wavelet filtering and PLS regression
Automatic affective dimension recognition from facial expression continuously in naturalistic contexts is a very challenging research topic but very important in human-computer interaction. In this paper, an automatic recognition system was proposed to predict the affective dimensions such as Arousal, Valence and Dominance continuously in naturalistic facial expression videos. Firstly, visual and vocal features are extracted from image frames and audio segments in facial expression videos. Secondly, a wavelet transform based digital filtering method is applied to remove the irrelevant noise information in the feature space. Thirdly, Partial Least Squares regression is used to predict the affective dimensions from both video and audio modalities. Finally, two modalities are combined to boost overall performance in the decision fusion process. The proposed method is tested in the fourth international Audio/Visual Emotion Recognition Challenge (AVEC2014) dataset and compared to other state-of-the-art methods in the affect recognition sub-challenge with a good performance
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Real-time Emotional State Detection from Facial Expression on Embedded Devices
From the last decade, researches on human facial
emotion recognition disclosed that computing models built on
regression modelling can produce applicable performance.
However, many systems need extensive computing power to be
run that prevents its wide applications such as robots and smart
devices. In this proposed system, a real-time automatic facial
expression system was designed, implemented and tested on an
embedded device such as FPGA that can be a first step for a
specific facial expression recognition chip for a social robot. The
system was built and simulated in MATLAB and then was built
on FPGA and it can carry out real time continuously emotional
state recognition at 30 fps with 47.44% accuracy. The proposed
graphic user interface is able to display the participant video and
two dimensional predict labels of the emotion in real time
together.The research presented in this paper was supported partially by the Slovak Research and Development Agency under the research projects APVV-15-0517 & APPV-15-0731 and by the Ministry of Education, Science, Research and Sport of the Slovak Republic under the project VEGA 1/0075/15