Name
Abstract | ||
|---|---|---|
Multi-spectral face recognition has been attracting increasing interest. In the last decade, several multi-spectral face recognition methods have been presented. However, it has not been well studied that how to jointly learn effective features with favorable discriminability from multiple spectra even when multi-spectral face images are severely contaminated by noise. Multi-view dictionary learning is an effective feature learning technique, which learns dictionaries from multiple views of the same object and has achieved state-of-the-art classification results. In this paper, we for the first time introduce the multi-view dictionary learning technique into the field of multi-spectral face recognition and propose a multi-spectral low-rank structured dictionary learning (MLSDL) approach. It learns multiple structured dictionaries, including a spectrum-common dictionary and multiple spectrum-specific dictionaries, which can fully explore both the correlated information and the complementary information among multiple spectra. Each dictionary contains a set of class-specified sub-dictionaries. Based on the low-rank matrix recovery theory, we apply low-rank regularization in multi-spectral dictionary learning procedure such that MLSDL can well solve the problem of multi-spectral face recognition with high levels of noise. We also design the low-rank structural incoherence term for multi-spectral dictionary learning, so as to reduce the redundancy among multiple spectrum-specific dictionaries. In addition, to enhance the efficiency of classification procedure, we design a low-rank structured collaborative representation classification scheme for MLSDL. Experimental results on HK PolyU, CMU and UWA hyper-spectral face databases demonstrate the effectiveness of the proposed approach. We propose a multi-spectral low-rank structured dictionary learning approach.We learn spectrum-common dictionary and spectrum-specific dictionaries.Low-rank structured regularization and incoherence terms are designed.Low-rank structured collaborative representation classification is provided. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.patcog.2016.01.023 | Pattern Recognition |
Keywords | Field | DocType |
Multi-spectral face recognition,Multi-spectral low-rank structured dictionary learning,Spectrum-common dictionary,Spectrum-specific dictionary,Low-rank regularization | Facial recognition system,Dictionary learning,K-SVD,Pattern recognition,Computer science,Classification scheme,Redundancy (engineering),Regularization (mathematics),Artificial intelligence,Multi spectral,Feature learning,Machine learning | Journal |
Volume | Issue | ISSN |
59 | C | 0031-3203 |
Citations | PageRank | References |
28 | 0.71 | 34 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiao-Yuan Jing | 1 | 769 | 55.18 |
| Fei Wu | 2 | 124 | 7.11 |
| Xiaoke Zhu | 3 | 78 | 7.77 |
| Xiwei Dong | 4 | 103 | 7.91 |
| Fei Ma | 5 | 52 | 13.61 |
| Zhiqiang Li | 6 | 28 | 0.71 |