Manifold aware discriminant collaborative graph embedding for face recognition
| Autor: | Xiaoming Zhao, Songjiang Lou, Yanghui Ma |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Graph embedding
business.industry Computer science Dimensionality reduction Feature extraction Nonlinear dimensionality reduction Pattern recognition 02 engineering and technology 01 natural sciences Facial recognition system law.invention 010104 statistics & probability law 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) 020201 artificial intelligence & image processing Artificial intelligence 0101 mathematics Representation (mathematics) business Manifold (fluid mechanics) |
| Zdroj: | Tenth International Conference on Digital Image Processing (ICDIP 2018). |
| DOI: | 10.1117/12.2503280 |
| Popis: | Dimensionality reduction has been widely used to deal with high dimensional data. In this paper, based on manifold learning and collaborative representation, an efficient subspace learning algorithm named Manifold Aware Discriminant Collaborative Graph Embedding (MADCGE), is proposed for face recognition. Firstly, the representation coefficients of face images are obtained by collaborative representation combined with label information and manifold structure. Then, it constructs a new graph with the coefficients obtained as the adjacent weights. Lastly, graph embedding is exploited to learn an optimal projective matrix for feature extraction. As a result, the proposed algorithm avoids choosing the neighborhood size of graph, which is difficult in literature. More importantly, it can not only preserve the linear reconstructive relationships between samples, but also sufficiently utilize the merits of label information and nonlinear manifold structure to further improve the discriminative ability. Extensive experiments on face databases (AR face database and YALE-B face database) are conducted to exam the performance of the proposed scheme and the results demonstrate that the proposed method has better performance than some other used methods. |
| Databáze: | OpenAIRE |
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