Name
Abstract | ||
|---|---|---|
The current study put forward a multi-feature kernel discriminant dictionary learning algorithm for face recognition. It was based on the supervised within-class-similar discriminative dictionary learning algorithm (SCDDL) we introduced previously. The proposed new algorithm was thus named as multi-feature kernel SCDDL (MKSCDDL). In contrast to the weighted combination or the constraint of representation coefficients for the feature combination used by some popular methods, MKSCDDL introduced the multiple kernel learning technique into the dictionary learning scheme. The experimental results on three large well-known face databases suggested that combination multiple features in MKSCDDL improved the recognition rate compared with SCDDL. In addition, adopting multiple kernel learning technique resulted in an excellent multi-feature dictionary learning approach when compared with some state-of-the-art multi-feature algorithms such as multiple kernel learning and multi-task joint sparse representation methods, indicating the effectiveness of the multiple kernel learning technique in the combination of multiple features for classification. A multi-feature kernel discriminant DL algorithm for face recognition is proposed.Multiple kernel framework for multi-feature fusion is adopted into the DL scheme.The MKSCDDL could enhance the recognition rate compared with some other algorithms. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.patcog.2016.12.001 | Pattern Recognition |
Keywords | Field | DocType |
Multi-feature kernel discriminative dictionary learning,Face recognition,Multiple kernel learning | Semi-supervised learning,Pattern recognition,Radial basis function kernel,Computer science,Kernel embedding of distributions,Multiple kernel learning,Kernel Fisher discriminant analysis,Tree kernel,Polynomial kernel,Artificial intelligence,Kernel method,Machine learning | Journal |
Volume | Issue | ISSN |
66 | C | 0031-3203 |
Citations | PageRank | References |
10 | 0.44 | 26 |
Authors | ||
5 | ||