Sparse Illumination Learning and Transfer for Single-Sample Face Recognition with Image Corruption and Misalignment
| العنوان: | Sparse Illumination Learning and Transfer for Single-Sample Face Recognition with Image Corruption and Misalignment |
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| المؤلفون: | Yi Ma, Liansheng Zhuang, Tsung-Han Chan, Allen Y. Yang, S. Shankar Sastry |
| المصدر: | International Journal of Computer Vision. 114:272-287 |
| بيانات النشر: | Springer Science and Business Media LLC, 2014. |
| سنة النشر: | 2014 |
| مصطلحات موضوعية: | FOS: Computer and information sciences, Pixel, business.industry, Computer science, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Initialization, Pattern recognition, Sparse approximation, Facial recognition system, Sample (graphics), Artificial Intelligence, Face (geometry), Pattern recognition (psychology), Three-dimensional face recognition, Computer vision, Computer Vision and Pattern Recognition, Artificial intelligence, business, Software |
| الوصف: | Single-sample face recognition is one of the most challenging problems in face recognition. We propose a novel algorithm to address this problem based on a sparse representation based classification (SRC) framework. The new algorithm is robust to image misalignment and pixel corruption, and is able to reduce required gallery images to one sample per class. To compensate for the missing illumination information traditionally provided by multiple gallery images, a sparse illumination learning and transfer (SILT) technique is introduced. The illumination in SILT is learned by fitting illumination examples of auxiliary face images from one or more additional subjects with a sparsely-used illumination dictionary. By enforcing a sparse representation of the query image in the illumination dictionary, the SILT can effectively recover and transfer the illumination and pose information from the alignment stage to the recognition stage. Our extensive experiments have demonstrated that the new algorithms significantly outperform the state of the art in the single-sample regime and with less restrictions. In particular, the single-sample face alignment accuracy is comparable to that of the well-known Deformable SRC algorithm using multiple gallery images per class. Furthermore, the face recognition accuracy exceeds those of the SRC and Extended SRC algorithms using hand labeled alignment initialization. |
| تدمد: | 1573-1405 0920-5691 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aacfd98a595482d61e0295fbaef882b0Test https://doi.org/10.1007/s11263-014-0749-xTest |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....aacfd98a595482d61e0295fbaef882b0 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15731405 09205691 |
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