An EM based probabilistic two-dimensional CCA with application to face recognition
| Autor: | Seyed Hashem Ahmadi, Abdolreza Mirzaei, Mehran Safayani, Homayun Afrabandpey |
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| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Facial expression Loading factor Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Feature extraction Computer Science - Computer Vision and Pattern Recognition Probabilistic logic Machine Learning (stat.ML) 020206 networking & telecommunications Pattern recognition 02 engineering and technology Facial recognition system Machine Learning (cs.LG) Computer Science - Learning Statistics - Machine Learning Artificial Intelligence Robustness (computer science) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | Applied Intelligence. 48:755-770 |
| ISSN: | 1573-7497 0924-669X |
| DOI: | 10.1007/s10489-017-1012-2 |
| Popis: | Recently, two-dimensional canonical correlation analysis (2DCCA) has been successfully applied for image feature extraction. The method instead of concatenating the columns of the images to the one-dimensional vectors, directly works with two-dimensional image matrices. Although 2DCCA works well in different recognition tasks, it lacks a probabilistic interpretation. In this paper, we present a probabilistic framework for 2DCCA called probabilistic 2DCCA (P2DCCA) and an iterative EM based algorithm for optimizing the parameters. Experimental results on synthetic and real data demonstrate superior performance in loading factor estimation for P2DCCA compared to 2DCCA. For real data, three subsets of AR face database and also the UMIST face database confirm the robustness of the proposed algorithm in face recognition tasks with different illumination conditions, facial expressions, poses and occlusions. |
| Databáze: | OpenAIRE |
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