期刊名称：International Journal of Applied Mathematics and Computer Science
电子版ISSN：2083-8492
出版年度：2018
卷号：28
期号：2
页码：1-11
DOI：10.2478/amcs-2018-0030
出版社：De Gruyter Open
摘要：In recent years, research in automated facial expression recognition has attained significant attention for its potential applicability
in human–computer interaction, surveillance systems, animation, and consumer electronics. However, recognition
in uncontrolled environments under the presence of illumination and pose variations, low-resolution video, occlusion, and
random noise is still a challenging research problem. In this paper, we investigate recognition of facial expression in difficult
conditions by means of an effective facial feature descriptor, namely the directional ternary pattern (DTP). Given a
face image, the DTP operator describes the facial feature by quantizing the eight-directional edge response values, capturing
essential texture properties, such as presence of edges, corners, points, lines, etc. We also present an enhancement of
the basic DTP encoding method, namely the compressed DTP (cDTP) that can describe the local texture more effectively
with fewer features. The recognition performances of the proposed DTP and cDTP descriptors are evaluated using the
Cohn–Kanade (CK) and the Japanese female facial expression (JAFFE) database. In our experiments, we simulate difficult
conditions using original database images with lighting variations, low-resolution images obtained by down-sampling the
original, and images corrupted with Gaussian noise. In all cases, the proposed method outperforms some of the well-known
face feature descriptors.
