Abstract
Face recognition technology has evolved as an enchanting solution to address the contemporary needs in order to perform identification and verification of identity claims. By advancing the feature extraction methods and dimensionality reduction techniques in the application of pattern recognition, a number of face recognition systems has been developed with distinct degrees of success. Locality preserving projection (LPP) is a recently proposed method for unsupervised linear dimensionality reduction. LPP preserve the local structure of face image space which is usually more significant than the global structure preserved by principal component analysis (PCA) and linear discriminant analysis (LDA). This paper focuses on a systematic analysis of locality-preserving projections and the application of LPP in combination with an existing technique This combined approach of LPP through MPCA can preserve the global and the local structure of the face image which is proved very effective. Proposed approach is tested using the AT & T face database. Experimental results show the significant improvements in the face recognition performance in comparison with some previous methods.
Highlights
Biometric technologies have been evolved as an enchanting solution to perform secure identification and personal verification
Most common feature extraction methods are principal component analysis (PCA)[1] and linear discriminant analysis (LDA) [2]. Another linear technique which is used for face recognition is Locality Preserving Projections (LPP) [3],[4], which finds an embedding that preserves local information, and gains a face subspace that best detects the essential face manifold structure[9],[11]
When high-dimensional data lies on a low dimension manifold embedded in the data space, LPP approximate the eigenfunctions of the LaplaceBeltrami operator of the manifold
Summary
Biometric technologies have been evolved as an enchanting solution to perform secure identification and personal verification. We are projecting the face data onto a Multilinear Principal Component Analysis (MPCA) subspace, and LPP algorithm is further used to preserve the local structure information. This combined approach considering the global and local structure of the face image space can obtain a more effective optimal subspace for face representation and recognition. It compresses and preserves the principal information in a matrix form, so it removes more inherent redundancy, and a much lower http://ijacsa.thesai.org/.
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