4.6 Article

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

期刊

SENSORS
卷 21, 期 22, 页码 -

出版社

MDPI
DOI: 10.3390/s21227498

关键词

deep learning; stress recognition; stress database; spatial attention; temporal attention; facial landmark

资金

  1. Institute of Information & communications Technology Planning & Evaluation (IITP)
  2. Korea government, Ministry of Science and ICT (MSIT) [2016-0-00197]

向作者/读者索取更多资源

With increasing interest in stress control, many studies on stress recognition have been conducted, focusing on physiological signals and facial images. However, both methods have their limitations. To address these challenges, a deep-learning-based stress-recognition method was proposed, utilizing a large image database and temporal and spatial attention mechanisms for improved performance.
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 highly related to stress, as well as spatial attention, which assigns a high weight to regions that are highly related to stress. By adding a network that inputs the facial landmark information closely related to stress, we supplemented the network that receives only facial images as the input. Experimental results on our newly constructed database indicated that the proposed method outperforms contemporary deep-learning-based recognition methods.

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