JOURNAL ARTICLE

A non linear face recognition system using Support Vector Machine

Abstract

A face recognition system uses face to verify individuals using computing capability. However, its performances often degrade due to high dimensional data and large feature appearance of the face image. This paper present a face recognition system based on non linear feature extraction technique to reduce the dimensionality of the face image, called Locally Linear Embedding. This method considers the hidden layer of face manifold to be the input of a SVM multiclass classifier. The performance is evaluated using the ORL database and achieved better recognition rates than the Principal Component Analysis.

Keywords:
Facial recognition system Artificial intelligence Computer science Pattern recognition (psychology) Feature extraction Principal component analysis Support vector machine Dimensionality reduction Face (sociological concept) Embedding Three-dimensional face recognition Nonlinear dimensionality reduction Classifier (UML) Computer vision Face detection

Metrics

3
Cited By
0.28
FWCI (Field Weighted Citation Impact)
12
Refs
0.60
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Face and Expression Recognition
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Face recognition and analysis
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Biometric Identification and Security
Physical Sciences →  Computer Science →  Signal Processing

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