Facial expression recognition with fused handcraft features based on pixel difference local directional number pattern
文献类型： 外文期刊
作者： Wang, Yan 1 ; Wang, Jianchun 2 ; Li, Yanju 2 ; Yu, Ming 3 ; Zhou, Yancong 1 ; Zhang, Bo 1 ;
作者机构： 1.Tianjin Univ Commerce, Coll Informat Engn, Guangrong Rd 409, Tianjin 300134, Peoples R China
2.Tianjin Acad Agr Sci, Inst Informat, Agr Informat Dept, Tianjin, Peoples R China
3.Hebei Univ Technol, Sch Artificial Intelligence, Tianjin, Peoples R China
关键词： Facial expression recognition; LDN; feature fusion; softmax
期刊名称：JOURNAL OF INTELLIGENT & FUZZY SYSTEMS （ 影响因子：1.851； 五年影响因子：1.797 ）
ISSN： 1064-1246
年卷期： 2021 年 41 卷 1 期
页码：
收录情况： SCI
摘要： Facial expression recognition (FER) has been an active research area in recent years, which plays a vital role in national security and human-computer interaction. Due to the lacking of sufficient expression features and facial images, it is challenging to automatically recognize facial expression with high accuracy. In this paper, we propose a fusion handcraft feature method to improve FER from images. Firstly, a new texture feature extraction method PD-LDN (Pixel Difference Local Directional Number pattern) is proposed, which can extract more local information, reduce noise disturbance and feature dimension. Secondly, the handcrafted features including PD-LDN texture features, geometric features, and BOVW (Bag of Visual Words) semantic features are connected in parallel to an improved autoencoder network for fusion. Finally, the fused features are input into the softmax classifier for recognizing facial expression. We conduct extensive experiments on JAFFE and CK+datasets. Our proposed method shows superior performance than the state-of-the-art approaches on recognizing facial expressions.
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