计算机科学
人工智能
模式识别(心理学)
面部表情识别
面部表情
特征(语言学)
特征提取
面部识别系统
面子(社会学概念)
计算机视觉
语音识别
作者
Ruicong Zhi,Mengyi Liu,Hairui Xu,Ming Wan
出处
期刊:Communications in computer and information science
日期:2019-12-16
卷期号:: 301-311
被引量:1
标识
DOI:10.1007/978-981-15-1925-3_22
摘要
Automatic facial micro-expression recognition is challenging for the subtlety and transience in facial motion, and limited databases. Most researches focus on handcrafted techniques for facial micro-expression analysis on two-dimensional images. However, spatiotemporal facial feature representation is a critical issue for facial micro-expression recognition due to its short duration and subtle facial movement. To deeply extract the appearance characteristics and facial changes effectively from facial image sequences, a feature-wise deep learning model was proposed by applying temporal Convolutional Neural Network (3D-CNN) and Long Short-Term Memory (LSTM) to enhance temporal feature learning. There are two stages involved: (1) The CNN was extended to convolute along spatio and temporal simultaneously, to better represent the facial texture and motion. (2) The feature vector obtained by 3D-CNN was fed into LSTM for temporal enrichment. It was demonstrated that the proposed model achieved promising good performance on CASME II and SMIC databases on person-independent and cross-database experiments.
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