Abstract
Feature extraction techniques are widely used to reduce the complexity high dimensional data. Nonlinear feature extraction via Locally Linear Embedding (LLE) has attracted much attention due to their high performance. In this paper, we proposed a novel approach for face recognition to address the challenging task of recognition using integration of nonlinear dimensional reduction Locally Linear Embedding integrated with Local Fisher Discriminant Analysis (LFDA) to improve the discriminating power of the extracted features by maximize between-class while within-class local structure is preserved. Extensive experimentation performed on the CMU-PIE database indicates that the proposed methodology outperforms Benchmark methods such as Principal Component Analysis (PCA), Fisher Discrimination Analysis (FDA). The results showed that 95% of recognition rate could be obtained using our proposed method.
Highlights
During the past decades, Face Recognition (FR) has become an active area of research in computer vision, neuroscience and psychology
In this paper we propose a new method to overcome the limitation of Principal Component Analysis (PCA) and overcome the weakness of Fisher Discrimination Analysis (FDA) by integrating Locally Nonlinear Embedding (LLE) proposed by Saul and Roweis[14] and Local Fisher Discriminating Analysis (LFDA) recently reported Sugiyama[18], Locally Linear Embedding (LLE) is Manifold learning approaches which is aimed to discover the intrinsical low dimensional variables from high dimensional nonlinear data Local Fisher Discriminant Analysis (LFDA) algorithm is used to project samples into discriminant space to attain between-class separation and within class local structure preservation at the same time
We have proposed a novel approach method called LLELFDA based on nonlinear feature extraction
Summary
Face Recognition (FR) has become an active area of research in computer vision, neuroscience and psychology. Two issues are central to face recognition algorithms (i) feature selection for face representation and (ii) classification of a new face image based on the chosen feature representation. In face recognition, such as Eigenfaces by Matthew and Pentland[1] and Eigenfaces proposed by Belhumeur and Hespanha [2] are two well-known linear projection methods for data reduction and feature extraction under the unsupervised and supervised learning settings, respectively. One of the most successful face recognition methods based on linearly projecting the image space to a low dimensional subspace it finds the optimal projection directions that maximally preserve the data variance.
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