Dual-Modal Emotion Recognition Based on Facial Expression and Body Posture in Video Sequences

Mingxing, 姜明星 Jiang; Min, 胡敏 Hu; Xiaohua, 王晓华 Wang; Fuji, 任福继 Ren; Haowen, 王浩文 Wang

doi:10.3788/lop55.071004

Cited by 2 publications

(1 citation statement)

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“…Wang et al [63] focused on the problem of high computational complexity in video emotion recognition and proposed a bimodal emotion recognition method based on temporal-spatial local binary pattern moment (TSLBPM) which has been proven effective. Jiang et al [64] proposed a spatiotemporal local ternary orientational pattern (SLTOP) feature description method and cloud-weighted decision fusion classification method for bimodal emotion recognition of facial expressions and postures in video sequences, achieving better recognition result than other classification recognition methods in the comparative experiments.…”

Section: Multimodal Emotion Recognitionmentioning

confidence: 99%

Advances in Emotion Recognition: Link to Depressive Disorder

Cheng¹,

Wang²,

Ouyang³

et al. 2020

Neurological and Mental Disorders

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Emotion recognition enables real-time analysis, tagging, and inference of cognitive affective states from human facial expression, speech and tone, body posture and physiological signal, as well as social text on social network platform. Recognition of emotion pattern based on explicit and implicit features extracted through wearable and other devices could be decoded through computational modeling. Meanwhile, emotion recognition and computation are critical to detection and diagnosis of potential patients of mood disorder. The chapter aims to summarize the main findings in the area of affective recognition and its applications in major depressive disorder (MDD), which have made rapid progress in the last decade.

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Section: Multimodal Emotion Recognitionmentioning

confidence: 99%