Facial expression recognition with local prominent directional pattern

被引:50
作者
Makhmudkhujaev, Farkhod [1 ]
Abdullah-Al-Wadud, M. [2 ]
Bin Iqbal, Md Tauhid [1 ]
Ryu, Byungyong [1 ]
Chae, Oksam [1 ]
机构
[1] Kyung Hee Univ, Dept Comp Sci & Engn, Yongin 17104, Gyeonggi Do, South Korea
[2] King Saud Univ, Dept Software Engn, Riyadh 11543, Saudi Arabia
基金
新加坡国家研究基金会;
关键词
LDPD; Histogram of directional variations; Prominent directions; Shape pattern; Facial expression recognition; JUNCTION DETECTION; FACE RECOGNITION; REPRESENTATION; DATABASE;
D O I
10.1016/j.image.2019.01.002
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Local edge-based descriptors have gained much attention as feature extraction methods for facial expression recognition. However, such descriptors are found to suffer from unstable shape representations for different local structures for their sensitivity to local distortions such as noise and positional variations. We propose a novel edge-based descriptor, named Local Prominent Directional Pattern (LPDP), which considers statistical information of a pixel neighborhood to encode more meaningful and reliable information than the existing descriptors for feature extraction. More specifically, LPDP examines a local neighborhood of a pixel to retrieve significant edges corresponding to the local shape and thereby ensures encoding edge information in spite of some positional variations and avoiding noisy edges. Thus LPDP can represent important textured regions much effectively to be used in facial expression recognition. Extensive experiments on facial expression recognition on well-known datasets also demonstrate the better capability of LPDP than other existing descriptors in terms of robustness in extracting various local structures originated by facial expression changes.
引用
收藏
页码:1 / 12
页数:12
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