Improving Cross-resolution Face Matching using Ensemble based Co-Transfer Learning (original) (raw)
2014, IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Face recognition algorithms are generally trained for matching high resolution images and they perform well for similar resolution test data. However, the performance of such systems degrade when a low resolution face image captured in unconstrained settings such as videos from cameras in a surveillance scenario are matched with high resolution gallery images. The primary challenge here is to extract discriminating features from limited biometric content in low resolution images and match it to information rich high resolution face images. The problem of cross-resolution face matching is further alleviated when there is limited labeled positive data for training face recognition algorithms. In this paper, the problem of cross-resolution face matching is addressed where low resolution images are matched with high resolution gallery. A co-transfer learning framework is proposed which is a cross-pollination of transfer learning and co-training paradigms and is applied for cross-resolut...
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