Real Time Eye Detector with Cascaded Convolutional Neural Networks
An accurate and efficient eye detector is essential for many computer vision applications. In this paper, we present an efficient method to evaluate the eye location from facial images. First, a group of candidate regions with regional extreme points is quickly proposed; then, a set of convolution n...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2018/1439312 |
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| _version_ | 1850237062864699392 |
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| author | Bin Li Hong Fu |
| author_facet | Bin Li Hong Fu |
| author_sort | Bin Li |
| collection | DOAJ |
| description | An accurate and efficient eye detector is essential for many computer vision applications. In this paper, we present an efficient method to evaluate the eye location from facial images. First, a group of candidate regions with regional extreme points is quickly proposed; then, a set of convolution neural networks (CNNs) is adopted to determine the most likely eye region and classify the region as left or right eye; finally, the center of the eye is located with other CNNs. In the experiments using GI4E, BioID, and our datasets, our method attained a detection accuracy which is comparable to existing state-of-the-art methods; meanwhile, our method was faster and adaptable to variations of the images, including external light changes, facial occlusion, and changes in image modality. |
| format | Article |
| id | doaj-art-02688de40b2e4d6b84378b11e8b8052e |
| institution | OA Journals |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-02688de40b2e4d6b84378b11e8b8052e2025-08-20T02:01:49ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322018-01-01201810.1155/2018/14393121439312Real Time Eye Detector with Cascaded Convolutional Neural NetworksBin Li0Hong Fu1Xi’an Institute of Optics and Precision Mechanics of CAS, Xi’an 710119, ChinaChu Hai College of Higher Education, Tuen Mun, Hong KongAn accurate and efficient eye detector is essential for many computer vision applications. In this paper, we present an efficient method to evaluate the eye location from facial images. First, a group of candidate regions with regional extreme points is quickly proposed; then, a set of convolution neural networks (CNNs) is adopted to determine the most likely eye region and classify the region as left or right eye; finally, the center of the eye is located with other CNNs. In the experiments using GI4E, BioID, and our datasets, our method attained a detection accuracy which is comparable to existing state-of-the-art methods; meanwhile, our method was faster and adaptable to variations of the images, including external light changes, facial occlusion, and changes in image modality.http://dx.doi.org/10.1155/2018/1439312 |
| spellingShingle | Bin Li Hong Fu Real Time Eye Detector with Cascaded Convolutional Neural Networks Applied Computational Intelligence and Soft Computing |
| title | Real Time Eye Detector with Cascaded Convolutional Neural Networks |
| title_full | Real Time Eye Detector with Cascaded Convolutional Neural Networks |
| title_fullStr | Real Time Eye Detector with Cascaded Convolutional Neural Networks |
| title_full_unstemmed | Real Time Eye Detector with Cascaded Convolutional Neural Networks |
| title_short | Real Time Eye Detector with Cascaded Convolutional Neural Networks |
| title_sort | real time eye detector with cascaded convolutional neural networks |
| url | http://dx.doi.org/10.1155/2018/1439312 |
| work_keys_str_mv | AT binli realtimeeyedetectorwithcascadedconvolutionalneuralnetworks AT hongfu realtimeeyedetectorwithcascadedconvolutionalneuralnetworks |