Deep-Learning-Based Stress Recognition with Spatial-Temporal Facial Information.

ABSTRACT: In recent times, as interest in stress control has increased, many studies on stress recognition have been conducted. Several studies have been based on physiological signals, but the disadvantage of this strategy is that it requires physiological-signal-acquisition devices. Another strategy employs facial-image-based stress-recognition methods, which do not require devices, but predominantly use handcrafted features. However, such features have low discriminating power. We propose a deep-learning-based stress-recognition method using facial images to address these challenges. Given that deep-learning methods require extensive data, we constructed a large-capacity image database for stress recognition. Furthermore, we used temporal attention, which assigns a high weight to frames that are

SUBMITTER: Jeon T 

PROVIDER: S-EPMC8625615 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature