Keystroke biometrics (KB) authentication systems are a less popular form of access control, although they are gaining popularity. In recent years, keystroke biometric authentication has been an active area of research...
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Keystroke biometrics (KB) authentication systems are a less popular form of access control, although they are gaining popularity. In recent years, keystroke biometric authentication has been an active area of research due to its low cost and ease of integration with existing security systems. various researchers have used different methods and algorithms for data collection, feature representation, classification, and performance evaluation to measure the accuracy of the system, and therefore achieved different accuracy rates. Although recently, the support vector machine is most widely used by researchers, it seems that ensemble methods and artificial neural networks yield higher accuracy. Moreover, the overall accuracy of KB is still lower than other biometric authentication systems, such as iris. The objective of this paper is to present a detailed survey of the most recent researches on keystroke dynamic authentication, the methods and algorithms used, the accuracy rate, and the shortcomings of those researches. Finally, the paper identifies some issues that need to be addressed in designing keystroke dynamic biometric systems, makes suggestions to improve the accuracy rate of KB systems, and proposes some possible future research directions.
We present a novel deep learning-based dehazing method using adaptive patch splits. Our method applies quad-tree decomposition to an input image, yielding multiple patches with adaptive sizes. Then, each patch is fed ...
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We present a novel deep learning-based dehazing method using adaptive patch splits. Our method applies quad-tree decomposition to an input image, yielding multiple patches with adaptive sizes. Then, each patch is fed into a Convolutional Neural Network (CNN) and classified into a single transmission value, in which a transmission map comprises transmission values from all patches. Homogeneous regions in the image are typically decomposed into large patches. Thus the method can save computational cost. Non-homogeneous regions are divided into small patches, which helps preserve local details in a transmission map. To train CNN, we synthesize numerous hazy images from haze-free images. Experimental results demonstrate our method surpasses state-of-the-art deep learning based algorithms quantitatively and qualitatively.
The sheer amount of personal data being transmitted to cloud services and the ubiquity of cellphones cameras and various sensors, have provoked a privacy concern among many people. On the other hand, the recent phenom...
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The sheer amount of personal data being transmitted to cloud services and the ubiquity of cellphones cameras and various sensors, have provoked a privacy concern among many people. On the other hand, the recent phenomenal growth of deep learning that brings advancements in almost every aspect of human life is heavily dependent on the access to data, including sensitive images, medical records, etc. Therefore, there is a need for a mechanism that transforms sensitive data in such a way as to preserves the privacy of individuals, yet still be useful for deep learning algorithms. This paper proposes the use of Generative Adversarial Networks (GANs) as one such mechanism, and through experimental results, shows its efficacy.
Color image can provide more information than gray image, so it is used more widely in the field of the communication. In recent years, how to safely encrypt images has received increasing attention. Numerous previous...
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Color image can provide more information than gray image, so it is used more widely in the field of the communication. In recent years, how to safely encrypt images has received increasing attention. Numerous previous image encryption algorithms are based on the symmetric encryption algorithm, but each pair of users communicating with symmetric encryption algorithm can only use the key that others do not know, so when the sender communicates with a receiver multiple times or sends the message to multiple receivers, the key number will grow at a geometric rate, and key management will become a burden on the users. In this paper, we propose an asymmetric image encryption algorithm for the advantages that the key groups and the number of keys in secret information transmission among multiple people are very small, and key transmission mode is relatively simple and secure. In our algorithm, first, the plain image is compressed and then the color image is encrypted by using the improved 4D cat map followed by asymmetric encryption which is based on elliptic curve ElGamal encryption, and finally, the encrypted image is globally diffused. The performance analysis is performed on key spaces, key sensitivity, the capability of resisting statistical attacks, differential attacks, known plaintext attacks and chosen plaintexticiphertext attacks and quality evaluation metrics of decrypted image. Simulation results show that the proposed algorithm has better security comparing with other algorithms. (C) 2017 Elsevier B.v. All rights reserved.
The data about the effect of image quality on accuracy of information parameters are presented. Studies on accuracy of measuring characteristics of objects in their images depending on image quality are described. Stu...
The data about the effect of image quality on accuracy of information parameters are presented. Studies on accuracy of measuring characteristics of objects in their images depending on image quality are described. Studies showed that accuracy of measurement algorithms improves with an increase in sample sizes of frames in the optoelectronic system (OES). image quality is improved by processing the frame samples with algorithms for increasing resolution and reducing noise levels, both in single-point and multi-position OESs based on digital cameras with matrix photodetectors. The maximum effect of an increase in information content of the image and accuracy of measuring algorithms is determined by variations in the pixel structure of each image in the region of brightness gradient boundaries. In the single-point OESs which ensure high quality of optical images without noise factors and variations of other external parameters, the optimal number of frames in the OES is four.
In the field of approximate nearest neighbor (ANN) search, rare of the existing approaches are tailored for video applications. The Ring Intersection Approximate Nearest Neighbor (RIANN) is the first ANN search algori...
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In the field of approximate nearest neighbor (ANN) search, rare of the existing approaches are tailored for video applications. The Ring Intersection Approximate Nearest Neighbor (RIANN) is the first ANN search algorithm for videos. It achieves real-time by performing the ANN search on the sparse grid and interpolating others. For some applications, the dense ANN search is needed to ensure the searching accuracy. To achieve dense ANN search in real-time, we consider the parallel computing as a solution. However, the RIANN algorithm is not suitable for parallel computing as the algorithm itself suffers from bad thread coherency. In this paper, we propose the Sphere Ring Intersection Approximate Nearest Neighbor (SRIANN), which solves the problem of bad thread coherency and improves the accuracy of ANN search compared to the original RIANN method. The experimental results show that the proposed method is the only one able to perform dense ANN search for CIF videos in real-time.
It is shown that the task of a person’s face recognition is one of the most popular at the present time. Its effective solution determines the reliability of many modern systems of personal identification, security, ...
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It is shown that the task of a person’s face recognition is one of the most popular at the present time. Its effective solution determines the reliability of many modern systems of personal identification, security, access control, and video surveillance at thermal and nuclear power plant stations. The speed of facial recognition algorithms is one of the key factors that determine the possibility of using for solving specific practical problems. It is shown that one of the methods for increasing the speed of facial recognition algorithms is the use of an integral form of data representation. The viola-Jones algorithm is identified as one of the classic approaches to solving the problem of face recognition. The main limitations for using the classical integral form of data representation are shown. Rotation of the head is identified as the main factor leading to a significant increase in the error in determining the brightness indicators for the most informative areas of the face. An approach based on the use of modified integrated forms for image frame data representation is proposed. These forms are oriented to the fast processing of information in the presence of a significant rotation of the head. The most important range of possible changes in the angle of rotation of the head is identified. The data structure is considered in case of using the modified integral form. Experimentally confirmed the possibility of using a limited number of modified forms of data representation while maintaining the high reliability of the work of the human face recognition algorithm.
A computer vision module is proposed for crack detection in tunnels, a challenging process due to the low visibility, the curvature from, and the structures of the cracks which, though being very narrow in width, they...
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A computer vision module is proposed for crack detection in tunnels, a challenging process due to the low visibility, the curvature from, and the structures of the cracks which, though being very narrow in width, they are very deep. Our system is embedded on a robot which surveys tunnels in real-time as it is moving in the infrastructure. Initially, a Convolutional Neural Network is employed to detect the cracks which, however, yields only approximate regions due to the great complexity of the scene. Then, a combined fuzzy spectral clustering is then introduced to refine the detected crack regions exploiting spatial and orientation coherency. The algorithms have been tested in real-life tunnels in Egnatia Highway. Our scheme yields high detection accuracy than existing methods and the capacity of the robot to touch the crack to allow in-situ measurements within a precision of 2-3cm in a tunnel of 7m height.
Canonical correlation analysis (CCA) is an effective feature learning method, which has wide applications in pattern recognition and computer vision. However, CCA considers the correlation only between the one-to-one ...
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Canonical correlation analysis (CCA) is an effective feature learning method, which has wide applications in pattern recognition and computer vision. However, CCA considers the correlation only between the one-to-one aligned samples in two views, ignoring the correlation between all the samples sharing the same label. In this paper, we propose a deep complete canonical correlation analysis (Deep Complete-CCA), which learns the relationships between all pairwise correspondences of sample points in the same classes. Unlike CCA, our method can learn discriminant representations that maximize the correlation between the two views while segregating the different classes on the learned space. We test Deep Complete-CCA on handwriting recognition and speech based emotion recognition using two popular MNIST and RAvDESS datasets. Experimental results show that our proposed method can obtain better performances than several related algorithms.
Currently decision-making systems get widespread. These systems are based on the analysis video sequences and also additional data. They are volume, change size, the behavior of one or a group of objects, temperature ...
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ISBN:
(数字)9781510608986
ISBN:
(纸本)9781510608979;9781510608986
Currently decision-making systems get widespread. These systems are based on the analysis video sequences and also additional data. They are volume, change size, the behavior of one or a group of objects, temperature gradient, the presence of local areas with strong differences, and others. Security and control system are main areas of application. A noise on the images strongly influences the subsequent processing and decision making. This paper considers the problem of primary signal processing for solving the tasks of image denoising and deblurring of multispectral data. The additional information from multispectral channels can improve the efficiency of object classification. In this paper we use method of combining information about the objects obtained by the cameras in different frequency bands. We apply method based on simultaneous minimization L2 and the first order square difference sequence of estimates to denoising and restoring the blur on the edges. In case of loss of the information will be applied an approach based on the interpolation of data taken from the analysis of objects located in other areas and information obtained from multispectral camera. The effectiveness of the proposed approach is shown in a set of test images.
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