Face Recognition using PCA and SVM with Surf Technique
نویسندگان
چکیده
منابع مشابه
Face Recognition Technique Using PCA, Wavelet and SVM
Biometric-based technologies include the identification based on physiological characteristics such as face, fingerprints, hand geometry, hand veins, palm, iris, retina, ear, voice and behavioral traits such as gait, signature and keystroke dynamics [1]. These biometric technologies require some voluntary action by the user. However, face recognition can be done passively without any explicit a...
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Face recognition is an important research field of pattern recognition. Up to now, it caused researchers great concern from these fields, such as pattern recognition and computer vision. In general, we can make sure that the performance of face recognition system is determined by how to extract feature vector exactly and to classify them into a class correctly. Therefore, it is necessary for us...
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In this study, we present an evaluation of using various methods for face recognition. As feature extracting techniques we benefit from wavelet decomposition and Eigenfaces method which is based on Principal Component Analysis (PCA). After generating feature vectors, distance classifier and Support Vector Machines (SVMs) are used for classification step. We examined the classification accuracy ...
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The Scale Invariant Feature Transform (SIFT) proposed by David G. Lowe has been used in face recognition and proved to perform well. Recently, a new detector and descriptor, named Speed-Up Robust Features (SURF) suggested by Herbert Bay, attracts people’s attentions. SURF is a scale and in-plane rotation invariant detector and descriptor with comparable or even better performance with SIFT. Bec...
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ژورنال
عنوان ژورنال: International Journal of Computer Applications
سال: 2015
ISSN: 0975-8887
DOI: 10.5120/ijca2015906832