Unsupervised multi-manifold linear differential projection(UMLDP) for face recognition
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A novel efficient algorithm called unsupervised multi-manifold linear differential projection(UMLDP) is proposed to overcome the drawbacks of existing unsupervised linear differential projection(ULDP) for face recognition. Firstly, the multi-manifold local neighborhood graph and the largest global variance is constructed respectively. Next, we calculate a low dimensional manifold embedded in high-dimensional space through the multi-objective optimization. This mapping can not only get the low-dimensional manifolds embedded in a high-dimensional space but also maintain the local and the global structural information effectively. Finally, experimental results validate the effectiveness of the proposed algorithm on the ORL, Yale and AR face databases.
KeywordsFace recognition Feature extraction Multi-manifold Unsupervised linear differential projection (ULDP)
This work is supported by the National Natural Science Fund of China (Grant Nos. 61503195, 61462064, 61203243,61402231, 61603192 and 61272077), the Natural Science Fund of Jiangsu Province(Grant No. BK20161580), the University Natural Science Fund of JiangSu Province, China (Grant No.15KJB520018, 16KJB520020 and 12KJA63001).
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