Facial Expression Recognition under Difficult Conditions: A Comprehensive Study on Edge Directional Texture Patterns

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.

Compact Local Directional Texture Pattern for Local Image Description

This paper presents an effective local image feature region descriptor, called CLDTP descriptor (Compact Local Directional Texture Pattern), and its application in image matching and object recognition. The CLDTP descriptor encodes the directional and contrast information in a local region, so it contains the gradient orientation information and the gradient magnitude information. As the dimension of the CLDTP histogram is much lower than the dimension of the LDTP histogram, the CLDTP descriptor has higher computational efficiency and it is suitable for image matching. Extensive experiments have validated the effectiveness of the designed CLDTP descriptor.

Paper Currency Image Features Extraction and Recognition Based on Gabor Filter

The paper currency image recognition method based on Gabor filter set is discussed in this paper. According to the paper currency image features, the suitable parameters of Gabor filter set are selected for the extraction of paper currency characteristics, the multi-scale and multi-directional texture characteristics of paper currency image are gotten; then the texture images are meshed, and the row and column projection sum of grid pixels' average grey are calculated, finally, the template match method based on grid projection characteristics is used for paper currency recognition. Experiments show that, this method has strong anti-interference ability, it can raise the recognition rate of old or dirty paper currency greatly, and it costs little time.