Research Article Detecting Happiness Using Hyperspectral Imaging Technology Min Hao , 1,2 Guangyuan Liu , 1,2 Anu Gokhale, 3 YaXu, 4 andRuiChen 5 1 School of Electronic and Information Engineering, Southwest University, Chongqing, China 2 Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing, Southwest University, Chongqing, China 3 Illinois State University, Normal, IL, USA 4 Center of Technical Support for Network Security, Chongqing Municipal Public Security Bureau, Chongqing, China 5 College of Computer and Information Science, Southwest University, Chongqing, China Correspondence should be addressed to Guangyuan Liu; liugy@swu.edu.cn Received 15 August 2018; Revised 22 November 2018; Accepted 3 December 2018; Published 15 January 2019 Academic Editor: Laura Marzetti Copyright © 2019 Min Hao et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Hyperspectral imaging (HSI) technology can be used to detect human emotions based on the power of material discrimination from their faces. In this paper, HSI is used to remotely sense and distinguish blood chromophores in facial tissues and acquire an evaluation indicator (tissue oxygen saturation, StO 2 ) using an optical absorption model. is study explored facial analysis while people were showing spontaneous expressions of happiness during social interaction. Happiness, as a psychological emotion, has been shown to be strongly linked to other activities such as physiological reaction and facial expression. Moreover, facial ex- pression as a communicative motor behavior likely arises from musculoskeletal anatomy, neuromuscular activity, and individual personality. is paper quantified the neuromotor movements of tissues surrounding some regions of interest (ROIs) on smiling happily. Next, we selected six regions—the forehead, eye, nose, cheek, mouth, and chin—according to a facial action coding system (FACS). Nineteen segments were subsequently partitioned from the above ROIs. e affective data (StO 2 ) of 23 young adults were acquired by HSI while the participants expressed emotions (calm or happy), and these were used to compare the significant differences in the variations of StO 2 between the different ROIs through repeated measures analysis of variance. Results demonstrate that happiness causes different distributions in the variations of StO 2 for the above ROIs; these are explained in depth in the article. is study establishes that facial tissue oxygen saturation is a valid and reliable physiological indicator of happiness and merits further research. 1.Introduction ere is a growing interest in the more positive emotions such as happiness [1–3]. Moreover, a state of happiness can overcome negative emotions such as stress [4, 5]. When people are engaged in social experiences that make them feel happy, people may exhibit measurable physiological char- acteristics such as blushing, facial expression features such as smiling, and body behavior such as dancing. With the ex- ception of facial expression, specifically, happiness pre- sumably includes a greater involvement of, for example, human physiology, psychology, behavior, and other human factors. Comprising a complex mix of behavioral, facial, physiological, and psychological traits, happiness is sus- pected to play a key role in many fields, including task execution [6, 7], healthcare [8, 9], and teaching and learning [10, 11]. 2.ReviewoftheLiterature Various in-depth studies have explored human emotions in terms of facial expressions. Witt and Flores-Mir [12, 13] and Janson et al. [14] investigated the facial smile paradigm by observing the subjects’ lips and dentition. Arigbabu et al. [15] investigated smile detection from face images in un- constrained environments. A proposed framework provided a very competitive detection rate and exploited image alignment as an important stage for improving the per- formance of smile detection. Of course, the studies based on facial expressions mostly are based on the hypothesis that Hindawi Computational Intelligence and Neuroscience Volume 2019, Article ID 1965789, 16 pages https://doi.org/10.1155/2019/1965789