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Descripción
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| This paper presents an algorithm for feature selection based on Particle Swarm Optimization (PSO) for Thermal Face Recognition. The total algorithm goes through many steps. In the very first step, thermal human face image is preprocessed and cropped the face region only from the entire image. In the next step, Scale Invariant Feature Transform (SIFT) is used to extract the features from the cropped face region. The features obtained by SIFT are invariant to object rotation and scale. But, some irrelevant and noisy features could be produced with the actual features. Unwanted features have to be removed. In other word, optimum features have to be selected for better Recognition accuracy. Since PSO is an optimization method, which works with the principle of local as well as global searches for finding optimum set of features, here this process has been implemented to select a subset of features that effectively represents original feature extracted for better classification convergence. Finally, minimum distance classifier is used to find the class label of each testing images. Minimum distance classifier acts as an objective function for PSO. In this work, all the experiments have been performed on UGC-JU thermal face database created at our own laboratory. The maximum success of 98.61% recognition has been achieved using SIFT and PSO for frontal face images and 90.28% for all images. | |
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Internacional
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Si |
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DOI
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10.1007/978-81-322-1985-9_2 |
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Edición del Libro
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1 |
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Editorial del Libro
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Springer Link |
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ISBN
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978-81-322-1984-2 |
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Serie
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Advances in Intelligent Systems and Computing |
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Título del Libro
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Applied Computation and Security Systems |
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Desde página
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25 |
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Hasta página
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35 |