Modeling observer happiness from facial hyperspectral sensor. Issue 1 (13th January 2020)

• Record Type: Journal Article
• Authors: Hao, Min; Liu, Guangyuan; Xie, Desheng; Ye, Ming; Cai, Jing
• Is Part Of: Engineering computations. Volume 37:Issue 1(2020)
• Page Start: 161
• Page End: 180
• Publication Date: 2020-01-13
• Subjects: Happiness -- Facial analysis -- Hyperspectral imaging -- Local dynamic patterns -- Tissue oxygen saturation; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-03-2019-0127
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: Happiness is an important mental emotion and yet becoming a major health concern nowadays. For this reason, better recognizing the objective understanding of how humans respond to event-related observations in their daily lives is especially important. Design/methodology/approach: This paper uses non-intrusive technology (hyperspectral imaging [HSI]) for happiness recognition. Experimental setup is conducted for data collection in real-life environments where observers are showing spontaneous expressions of emotions (calm, happy, unhappy: angry) during the experimental process. Based on facial imaging captured from HSI, this work collects our emotional database defined as SWU Happiness DB and studies whether the physiological signal (i.e. tissue oxygen saturation [StO2], obtained by an optical absorption model) can be used to recognize observer happiness automatically. It proposes a novel method to capture local dynamic patterns (LDP) in facial regions, introducing local variations in facial StO2 to fully use physiological characteristics with regard to hyperspectral patterns. Further, it applies a linear discriminant analysis-based support vector machine to recognize happiness patterns. Findings: The results show that the best classification accuracy is 97.89 per cent, objectively demonstrating a feasible application of LDP features on happiness recognition. Originality/value: This paper proposes a novel feature (i.e. LDP) to represent the local variations in facial StO2 for modeling the active happiness. It provides a possible extension to the promising practical application.