Illumination Removal via Gaussian Difference L0 Norm Model for Facial Experssion Recognition
Face images in the logarithmic space can be considered as a sum of the texture component and lighting map component according to Lambert Reflection. However, it is still not easy to separate these two parts, because face contour boundaries and lighting change boundaries are difficult to distinguish....
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/12/2667 |
| _version_ | 1797593638761398272 |
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| author | Xiaohe Li Wankou Yang |
| author_facet | Xiaohe Li Wankou Yang |
| author_sort | Xiaohe Li |
| collection | DOAJ |
| description | Face images in the logarithmic space can be considered as a sum of the texture component and lighting map component according to Lambert Reflection. However, it is still not easy to separate these two parts, because face contour boundaries and lighting change boundaries are difficult to distinguish. In order to enhance the separation quality of these to parts, this paper proposes an illumination standardization algorithm based on extreme L0 Gaussian difference regularization constraints, assuming that illumination is massively spread all over the image but illumination change boundaries are simple, regular, and sparse enough. The proposed algorithm uses an iterative L0 Gaussian difference smoothing method, which achieves a more accurate lighting map estimation by reserving the fewest boundaries. Thus, the texture component of the original image can be restored better by simply subtracting the lighting map estimated. The experiments in this paper are organized with two steps: the first step is to observe the quality of the original texture restoration, and the second step is to test the effectiveness of our algorithm for complex face classification tasks. We choose the facial expression classification in this step. The first step experimental results show that our proposed algorithm can effectively recover face image details from extremely dark or light regions. In the second step experiment, we use a CNN classifier to test the emotion classification accuracy, making a comparison of the proposed illumination removal algorithm and the state-of-the-art illumination removal algorithm as face image preprocessing methods. The experimental results show that our algorithm works best for facial expression classification at about 5 to 7 percent accuracy higher than other algorithms. Therefore, our algorithm is proven to provide effective lighting processing technical support for the complex face classification problems which require a high degree of preservation of facial texture. The contribution of this paper is, first, that this paper proposes an enhanced TV model with an L0 boundary constraint for illumination estimation. Second, the boundary response is formulated with the Gaussian difference, which strongly responds to illumination boundaries. Third, this paper emphasizes the necessity of reserving details for preprocessing face images. |
| first_indexed | 2024-03-11T02:11:08Z |
| format | Article |
| id | doaj.art-307eded985f44096b92fa3969dc97df8 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T02:11:08Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-307eded985f44096b92fa3969dc97df82023-11-18T11:28:07ZengMDPI AGMathematics2227-73902023-06-011112266710.3390/math11122667Illumination Removal via Gaussian Difference L0 Norm Model for Facial Experssion RecognitionXiaohe Li0Wankou Yang1Department of Network Information Security, Guangdong Police College, Guangzhou 510300, ChinaSchool of Automation, Southeast University, Nanjing 211189, ChinaFace images in the logarithmic space can be considered as a sum of the texture component and lighting map component according to Lambert Reflection. However, it is still not easy to separate these two parts, because face contour boundaries and lighting change boundaries are difficult to distinguish. In order to enhance the separation quality of these to parts, this paper proposes an illumination standardization algorithm based on extreme L0 Gaussian difference regularization constraints, assuming that illumination is massively spread all over the image but illumination change boundaries are simple, regular, and sparse enough. The proposed algorithm uses an iterative L0 Gaussian difference smoothing method, which achieves a more accurate lighting map estimation by reserving the fewest boundaries. Thus, the texture component of the original image can be restored better by simply subtracting the lighting map estimated. The experiments in this paper are organized with two steps: the first step is to observe the quality of the original texture restoration, and the second step is to test the effectiveness of our algorithm for complex face classification tasks. We choose the facial expression classification in this step. The first step experimental results show that our proposed algorithm can effectively recover face image details from extremely dark or light regions. In the second step experiment, we use a CNN classifier to test the emotion classification accuracy, making a comparison of the proposed illumination removal algorithm and the state-of-the-art illumination removal algorithm as face image preprocessing methods. The experimental results show that our algorithm works best for facial expression classification at about 5 to 7 percent accuracy higher than other algorithms. Therefore, our algorithm is proven to provide effective lighting processing technical support for the complex face classification problems which require a high degree of preservation of facial texture. The contribution of this paper is, first, that this paper proposes an enhanced TV model with an L0 boundary constraint for illumination estimation. Second, the boundary response is formulated with the Gaussian difference, which strongly responds to illumination boundaries. Third, this paper emphasizes the necessity of reserving details for preprocessing face images.https://www.mdpi.com/2227-7390/11/12/2667illuminationexpression recognitionGaussian difference |
| spellingShingle | Xiaohe Li Wankou Yang Illumination Removal via Gaussian Difference L0 Norm Model for Facial Experssion Recognition Mathematics illumination expression recognition Gaussian difference |
| title | Illumination Removal via Gaussian Difference L0 Norm Model for Facial Experssion Recognition |
| title_full | Illumination Removal via Gaussian Difference L0 Norm Model for Facial Experssion Recognition |
| title_fullStr | Illumination Removal via Gaussian Difference L0 Norm Model for Facial Experssion Recognition |
| title_full_unstemmed | Illumination Removal via Gaussian Difference L0 Norm Model for Facial Experssion Recognition |
| title_short | Illumination Removal via Gaussian Difference L0 Norm Model for Facial Experssion Recognition |
| title_sort | illumination removal via gaussian difference l0 norm model for facial experssion recognition |
| topic | illumination expression recognition Gaussian difference |
| url | https://www.mdpi.com/2227-7390/11/12/2667 |
| work_keys_str_mv | AT xiaoheli illuminationremovalviagaussiandifferencel0normmodelforfacialexperssionrecognition AT wankouyang illuminationremovalviagaussiandifferencel0normmodelforfacialexperssionrecognition |