Facial gesture recognition using two-channel bio-sensors configuration and fuzzy classifier: A pilot study
Facial gesture recognition using two-channel bio-sensors configuration and fuzzy classifier: A pilot study
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使用双通道生物传感器配置和模糊分类器的面部手势识别：试点研究
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
M. Firoozabadi
中科院分区：
文献类型：
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作者：
M. Hamedi;I. Mohammad Rezazadeh;M. Firoozabadi
人脸姿态识别已经成为诊断、医疗和工业领域的一个重要问题。人脸姿态的自动识别可以被认为是人机接口应用中的一个重要因素。基于表面肌电信号（SEMG）的人脸姿态识别在近十年来得到了广泛的研究。表面肌电信号记录了面部肌肉的电位，因此对面部手势识别具有准确率。提出了一种基于前额双通道生物电信号的5种不同表情识别方法。记录的信号进行了处理，在四个步骤：过滤，特征提取（RMS），阈值，和分类。利用模糊C均值（FCM）分类器将提取的特征分为5类（休息，微笑，皱眉，愤怒，和手势'刻痕'通过拔眉毛）。最后，将我们的方法应用于4个主题，取得了90.8%的识别率。
Facial gesture recognition has become an important issue in diagnostic, medical and industrial fields. Automatic recognition of facial gestures could be considered as an important factor in human-machine interface applications. Facial gesture recognition based on surface electromyography (SEMG) has been well thought-out in the recent decade. SEMG has accurate rates for facial gesture recognition since it records the electrical potential from facial muscles. This paper presents a method for recognizing 5 different facial gestures based on forehead two-channels bioelectric-signals. The recorded signals were processed in four steps: filtration, feature extraction (RMS), thresholding, and classification. The extracted features were classified into 5 facial gesture classes (rest, smile, frown, rage, and gesturing ‘notch’ by pulling up the eyebrows) by utilizing Fuzzy C-Means (FCM) classifier. Finally 90.8% recognition ratio has been achieved by applying our method on 4 subjects.