In the field of Facial Expression Recognition (FER), traditional local texture coding methods have a low computational complexity, while providing a robust solution with respect to occlusion, illumination, and other f...
详细信息
In the field of Facial Expression Recognition (FER), traditional local texture coding methods have a low computational complexity, while providing a robust solution with respect to occlusion, illumination, and other factors. However, there is still need for improving the accuracy of these methods while maintaining their real-time nature and low computational complexity. In this paper, we propose a feature-based FER system with a novel local texture coding operator, named central symmetric local gradient coding (CS-LGC), to enhance the performance of real-time systems. It uses four different directional gradients on 5 x 5 grids, and the gradient is computed in the center-symmetric way. The averages of the gradients are used to reduce the sensitivity to noise. These characteristics lead to symmetric of features by the CS-LGC operator, thus providing a better generalization capability in comparison to existing local gradient coding (LGC) variants. The proposed system further transforms the extracted features into an eigen-space using a principal component analysis (PCA) for better representation and less computation;it estimates the intended classes by training an extreme learning machine. The recognition rate for the JAFFE database is 95.24%, whereas that for the CK+ database is 98.33%. The results show that the system has advantages over the existing local texture coding methods.
Automatic facial expression recognition has always been a challenging task to understand human behavior from real world images. Certain type of issues are associated with such images that include poor illumination, di...
详细信息
Automatic facial expression recognition has always been a challenging task to understand human behavior from real world images. Certain type of issues are associated with such images that include poor illumination, different orientations and varying pose. The proposed technique first applies Fast Fourier Transform and Contrast Limited Adaptive Histogram Equalization (FFT+ CLAHE) method to compensate the poor illumination. Then merged binary pattern code (MBPC) is generated for every pixel. Two bits per neighbourhood are produced to form a 16-bit code per pixel. This code merges local features to improve the effectiveness of facial expression recognition system. MBPC descriptor captures changes along fine edges and prominent pattern around eyes, eye brows, mouth, bulges and wrinkles of the face. The results of proposed technique are compared with different variants of LBP and LGC based techniques for both holistic and zoned images. Static Facial Expression in Wild (SFEW) dataset is selected for experimentation. Results clearly indicate that the suggested MBPC based technique surpasses other techniques with 96.5% and 67.2% accuracy for holistic and division based approach respectively. Moreover, results indicate that the performance of holistic approach is much higher than division based approach. (C) 2018 Elsevier GmbH. All rights reserved.
The traditional local Binary Pattern (LBP) algorithm can analyze the center pixel and neighboring pixels of the gray relationship, using in facial expression recognition, but you cannot consider the eyes, mouth, foreh...
详细信息
The traditional local Binary Pattern (LBP) algorithm can analyze the center pixel and neighboring pixels of the gray relationship, using in facial expression recognition, but you cannot consider the eyes, mouth, forehead and other areas in the expression feature different trends in the gradient direction. Firstly, we propose the local gradient coding (LGC) algorithm, though the binary encoding to the horizontal, vertical and diagonal gradients respectively, to produce the fusion characteristic, then this can fully describe the facial muscles texture, wrinkles and other local deformation of contains the expression information. On the other hand, in order to reduce the computational complexity, and to remove the redundant, while not lose the main information contained in the face texture expression. This paper proposes and optimizes a new LGC operator based on horizontal and diagonal gradient prior principle (LGC-HD). The experimental results from JAFFE database show that, LGC-HD algorithm is more quickly and effectively to extract facial expression feature than LGC algorithm. Comparing to the traditional LBP algorithm, LBP uniform pattern and Gabor filtering, this LGC-HD algorithm has a significant advantage in the recognition accuracy and run time. (C) 2014 Elsevier GmbH. All rights reserved.
暂无评论