Reduced isothermal feature set for long wave infrared (LWIR) face recognition

Donoso, Ramiro; Martín, César San; Hermosilla, Gabriel

doi:10.1016/j.infrared.2017.04.019

Cited by 5 publications

(1 citation statement)

References 16 publications

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“…Facial expression is one of the most powerful, natural, and universal signals for human beings to convey their emotional states and intentions regardless of national borders, race, and gender [1,2], and there were multitudinous related applications such as the health management [3], aided driving [4,5], and others [6][7][8][9]. In the earlier researches on facial expressions of emotion, Ekman and Friesen argued that human beings perceive certain basic emotions in the same way regardless of their cultural background, and they defined the typical facial expressions into 6 categories: anger, disgust, fear, happiness, sadness, and surprise [10,11].…”

Section: Introductionmentioning

confidence: 99%

Emotion Recognition of Students Based on Facial Expressions in Online Education Based on the Perspective of Computer Simulation

Wang

Niu

et al. 2020

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Online education has developed rapidly due to its irreplaceable convenience. Under the severe circumstances caused by COVID-19 recently, many schools around the world have delayed opening and adopted online education as one of the main teaching methods. However, the efficiency of online classes has long been questioned. Compared with traditional face-to-face classes, there is a lack of direct, timely, and effective communication and feedback between teachers and students in the online courses. Previous studies have shown that there is a close and stable relationship between a person’s facial expressions and emotions generally. From the perspective of computer simulation, a framework combining a face expression recognition (FER) algorithm with online courses platforms is proposed in this work. The cameras in the devices are used to collect students’ face images, and the facial expressions are analyzed and classified into 8 kinds of emotions by the FER algorithm. An online course containing 27 students conducted on Tencent Meeting is used to test the proposed method, and the result proved that this method performs robustly in different environments. This framework can also be applied to other similar scenarios such as online meetings.

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