Robust Face Image Matching under Illumination Variations
<p/> <p>Face image matching is an essential step for face recognition and face verification. It is difficult to achieve robust face matching under various image acquisition conditions. In this paper, a novel face image matching algorithm robust against illumination variations is proposed...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2004-01-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1155/S1110865704410014 |
| _version_ | 1818863592569569280 |
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| author | Yang Chyuan-Huei Thomas Lai Shang-Hong Chang Long-Wen |
| author_facet | Yang Chyuan-Huei Thomas Lai Shang-Hong Chang Long-Wen |
| author_sort | Yang Chyuan-Huei Thomas |
| collection | DOAJ |
| description | <p/> <p>Face image matching is an essential step for face recognition and face verification. It is difficult to achieve robust face matching under various image acquisition conditions. In this paper, a novel face image matching algorithm robust against illumination variations is proposed. The proposed image matching algorithm is motivated by the characteristics of high image gradient along the face contours. We define a new consistency measure as the inner product between two normalized gradient vectors at the corresponding locations in two images. The normalized gradient is obtained by dividing the computed gradient vector by the corresponding locally maximal gradient magnitude. Then we compute the average consistency measures for all pairs of the corresponding face contour pixels to be the robust matching measure between two face images. To alleviate the problem due to shadow and intensity saturation, we introduce an intensity weighting function for each individual consistency measure to form a weighted average of the consistency measure. This robust consistency measure is further extended to integrate multiple face images of the same person captured under different illumination conditions, thus making our robust face matching algorithm. Experimental results of applying the proposed face image matching algorithm on some well-known face datasets are given in comparison with some existing face recognition methods. The results show that the proposed algorithm consistently outperforms other methods and achieves higher than 93% recognition rate with three reference images for different datasets under different lighting conditions.</p> |
| first_indexed | 2024-12-19T10:18:13Z |
| format | Article |
| id | doaj.art-6332c416935647998931312923fe675a |
| institution | Directory Open Access Journal |
| issn | 1687-6172 1687-6180 |
| language | English |
| last_indexed | 2024-12-19T10:18:13Z |
| publishDate | 2004-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-6332c416935647998931312923fe675a2022-12-21T20:26:08ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802004-01-01200416769050Robust Face Image Matching under Illumination VariationsYang Chyuan-Huei ThomasLai Shang-HongChang Long-Wen<p/> <p>Face image matching is an essential step for face recognition and face verification. It is difficult to achieve robust face matching under various image acquisition conditions. In this paper, a novel face image matching algorithm robust against illumination variations is proposed. The proposed image matching algorithm is motivated by the characteristics of high image gradient along the face contours. We define a new consistency measure as the inner product between two normalized gradient vectors at the corresponding locations in two images. The normalized gradient is obtained by dividing the computed gradient vector by the corresponding locally maximal gradient magnitude. Then we compute the average consistency measures for all pairs of the corresponding face contour pixels to be the robust matching measure between two face images. To alleviate the problem due to shadow and intensity saturation, we introduce an intensity weighting function for each individual consistency measure to form a weighted average of the consistency measure. This robust consistency measure is further extended to integrate multiple face images of the same person captured under different illumination conditions, thus making our robust face matching algorithm. Experimental results of applying the proposed face image matching algorithm on some well-known face datasets are given in comparison with some existing face recognition methods. The results show that the proposed algorithm consistently outperforms other methods and achieves higher than 93% recognition rate with three reference images for different datasets under different lighting conditions.</p>http://dx.doi.org/10.1155/S1110865704410014robust image matchingface recognitionillumination variationsnormalized gradient |
| spellingShingle | Yang Chyuan-Huei Thomas Lai Shang-Hong Chang Long-Wen Robust Face Image Matching under Illumination Variations EURASIP Journal on Advances in Signal Processing robust image matching face recognition illumination variations normalized gradient |
| title | Robust Face Image Matching under Illumination Variations |
| title_full | Robust Face Image Matching under Illumination Variations |
| title_fullStr | Robust Face Image Matching under Illumination Variations |
| title_full_unstemmed | Robust Face Image Matching under Illumination Variations |
| title_short | Robust Face Image Matching under Illumination Variations |
| title_sort | robust face image matching under illumination variations |
| topic | robust image matching face recognition illumination variations normalized gradient |
| url | http://dx.doi.org/10.1155/S1110865704410014 |
| work_keys_str_mv | AT yangchyuanhueithomas robustfaceimagematchingunderilluminationvariations AT laishanghong robustfaceimagematchingunderilluminationvariations AT changlongwen robustfaceimagematchingunderilluminationvariations |