The importance of Intelligent Transportation System (ITS) is increasing because of increasing the number of vehicles on the roads. Automatic License Plate Detection (ALPD) is still a challenging task based on weather,...
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The description of surface textures in terms of repeated colorimetric and geometric local surface variations is a crucial task for several applications, such as object interpretation or style identification. Recently,...
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Iridology is one of the technologies that is used in medical sector for helping the medical staffs in analyzing the patient's health from the observed iris. The assessment is based on mapping that uses iridology c...
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Facial expression recognition is a challenging problem in computer vision as changes in the background and location of the same person may also lead to difficulties in the recognition. In order to improve the poor eff...
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Deriving an effective facial representation from original face images is a vital step for successful facial expression recognition. In this paper, we explore the effect of representing the information in facial expres...
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Biometrics is an inevitable scheme in today's scenario to grant authentication for different protectionsystems. Still single level of biometric techniques cannot meet the expected level of accuracy, multi biometri...
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In the recent time bioinformatics take wide field in image processing and computer vision. Gender classification is essentially the task of identifying the person gender based on the facial image. Currently the gender...
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Face recognition is the practice of recognizing a person based on an image of their face and is now turned into a prominent avenue of research. This technique allows the use of a person's facial images to validate...
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Face recognition is the practice of recognizing a person based on an image of their face and is now turned into a prominent avenue of research. This technique allows the use of a person's facial images to validate them to a secure system, criminal identification, passport verification, monitoring, and so on. Face recognition is now incorporated into a number of study fields such as machine visibility, pattern recognition, bioinformatics, etc., and is known as a potential research area. The main problem with face detection is how to accurately find the best attribute for face detection. A considerable number of innovative features extraction algorithms exist; they chiefly include three aspects: face geometry, face and statistical characteristics. As the name carries it, face recognition method uses images of people's faces as its primary source of investigation to identify individuals through digital images and video frames. Hence, proposed algorithms can be either one of geometric feature-based or appearance-based. In this thesis, local binary pattern (LBP) and Discrete Wavelet Transform (DWT) are used and implemented to obtain the feature of images by using ORL and Yale databases. Face will be characterized as recognized or unrecognized face after matching with the already saved dataset. We apply DWT and LBP on the input face images which provide us high performance and clearer image compared to other algorithms. Simulations results in this work showed that the proposed approaches based on DWT and LBP was achieved very high accuracy performance.
Fingerprint-based identification is the incredible mean of human authentication since ancient decades. Complex distortions involved during minutia-based matching of two impressions of the same finger make the matching...
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Nowadays, face biometric based access control systems are becoming ubiquitous in our daily life while they are still vulnerable to spoofing attacks. So developing robust and reliable methods to prevent such frauds is ...
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Nowadays, face biometric based access control systems are becoming ubiquitous in our daily life while they are still vulnerable to spoofing attacks. So developing robust and reliable methods to prevent such frauds is unavoidable. As deep learning techniques have achieved satisfactory performances in computer vision, they have also been applied to face spoofing detection. However, the numerous parameters in these deep learning based detection methods cannot be updated to optimum due to limited data. local binary pattern (LBP), effective features for face recognition, have been employed in face spoofing detection and obtained promising results. Considering the similarities between LBP extraction and convolutional neural network (CNN) that the former can be accomplished by using fixed convolutional filters, we propose a novel end-to-end learnable LBP network for face spoofing detection. Our network can significantly reduce the number of network parameters by combing learnable convolutional layers with fixed-parameter LBP layers that are comprised of sparse binary filters and derivable simulated gate functions. Compared with existing deep leaning based detection methods, the parameters in our fully connected layers are up to 64x savings. Conducting extensive experiments on two standard spoofing databases, i.e., Relay-Attack and CASIA-FA, our proposed LBP network substantially outperforms the state-of-the-art methods.
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