M.Tech(DECS) Student, DR.K.V Subba Reddy College of Engineering For Women,Kurnool, Andhra Pradesh, India.
In this paper we developed a new dimensionality reduction method, named Biomimetic Uncorrelated Locality Discriminate Projection (BULDP), for face recognition. It is based on unsupervised discriminate projection and two human bionic characteristics: principle of homology continuity and principle of heterogeneous similarity. With these two human bionic characteristics, we propose a novel adjacency coefficient representation, which does not only capture the category information between different samples, but also reflects the continuity between similar samples and the similarity between different samples. By applying this new adjacency coefficient into the unsupervised discriminant projection, it can be shown that we can transform the original data space into an uncorrelated discriminant subspace. A detailed solution of the proposed BULDP is given based on singular value decomposition. Moreover, we also develop a nonlinear version of our BULDP using kernel functions for nonlinear dimensionality reduction. The performance of the proposed algorithms is evaluated and compared with the state-of-the-art methods on four public benchmarks for face recognition. Experimental results show that the proposed BULDP method and its nonlinear version achieve much competitive recognition performance..
Mrs.NAZIA BEGUM,Mr.SYED NOORULLAH."Biometric Uncorrelated Face Recognition Using Unsupervised Learning". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.7, Issue 03,pp.13-18, March - 2020, URL :https://ijcert.org/ems/ijcert_papers/V7I303.pdf,
: Biomimetic Uncorrelated Locality Discriminate Projection (BULDP).Face recognization, Unsupervised Learning
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