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• W3145881105 abstract "With the rapiddevelopment of applications related to human-computer interaction, facialexpression recognition plays an important role in affective computingtechnologies and can benefit many applications in computer technology,security, behavioural research, and clinical investigations on patients withneuropsychiatric disorders. This research aims towards developing algorithmsand frameworks for facial expression recognition with a low computationalcomplexity, which are suitable for real-time applications. Instead ofdeveloping a real-time system for a specific application, developing thecomponents of facial expression recognition systems is the main focus of thisresearch. One of the significant components of a facial expressionrecognition system is facial feature extraction. Two feature extractionalgorithms, based on appearance and geometry, were developed for imagesequences. To assess the performance of these algorithms, simulations werecarried out on various datasets containing expressions of different intensities(e.g. apparent and subtle expressions) and complexity. The proposedappearance-based algorithm, known as Spatio-Temporal Texture Map (STTM),demonstrated its capability to extract subtle motions of facial expressions andattained superior performances with a low computational cost. Similarly, theproposed geometry-based feature extraction algorithm, based on ActiveAppearance Model (AAM), demonstrated its excellent performance in annotatinglandmark points on videos. It was found that the number of iterations requiredin AAM fitting could be reduced by updating the parameters frame-by-frame.Moreover, to take into account the temporal information of a video,neighbouring frames were considered in AAM fitting which improved theannotation performance. The geometric feature is further evaluated for facialexpression recognition and showed an excellent performance in this task. Keeping in mind the significance of the dynamics of facialexpressions, intensity estimation of facial expressions is also addressed inthis work. Another problem addressed is expressions accompanied by head movementswhich makes decoding the depicted expressions difficult. Even though researchin facial expression recognition has been active since the last two decades,these topics recently gained attention from researchers. In order to addressthe former problem, a framework which jointly recognizes facial expressions andestimates facial expression intensities from image sequences was developed. Theframework consists of k Nearest Neighbours (kNN), a weighting scheme, and achange-point detector. With low computational complexity, the proposedalgorithm showed its superior performance, especially in estimating facialexpression intensities. To address the latter problem, where the facialexpression is captured at several different angles, a representation formulti-view facial expression recognition was developed. The proposed algorithmshowed its excellent performance based on the evaluations againststate-of-the-art algorithms." @default.
• W3145881105 created "2021-04-13" @default.
• W3145881105 creator A5036838729 @default.
• W3145881105 date "2017-04-20" @default.
• W3145881105 modified "2023-09-24" @default.
• W3145881105 title "Feature Extraction and Representation Techniques for Facial Expression Analysis" @default.
• W3145881105 doi "https://doi.org/10.4225/03/58f803ba17d3b" @default.
• W3145881105 hasPublicationYear "2017" @default.
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