2,022 research outputs found

    Face recognition using principal component analysis

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    Current methods of face recognition use linear methods to extract features. This causes potentially valuable nonlinear features to be lost. Using a kernel to extract nonlinear features should lead to better feature extraction and, therefore, lower error rates. Kernel Principal Component Analysis (KPCA) will be used as the method for nonlinear feature extraction. KPCA will be compared with well known linear methods such as correlation, Eigenfaces, and Fisherfaces

    A Novel Approach to Face Recognition using Image Segmentation based on SPCA-KNN Method

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    In this paper we propose a novel method for face recognition using hybrid SPCA-KNN (SIFT-PCA-KNN) approach. The proposed method consists of three parts. The first part is based on preprocessing face images using Graph Based algorithm and SIFT (Scale Invariant Feature Transform) descriptor. Graph Based topology is used for matching two face images. In the second part eigen values and eigen vectors are extracted from each input face images. The goal is to extract the important information from the face data, to represent it as a set of new orthogonal variables called principal components. In the final part a nearest neighbor classifier is designed for classifying the face images based on the SPCA-KNN algorithm. The algorithm has been tested on 100 different subjects (15 images for each class). The experimental result shows that the proposed method has a positive effect on overall face recognition performance and outperforms other examined methods
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