One of the biometric authentications is iris recognition using template *** are many methods proposed for iris template matching such as local binary pattern (LBP) and Histogram of Oriented Gradients (HOG).In this pap...
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Facial expressions are an exciting study area within Computer apparition, affecting estimating, and human-calculating interaction. Our approach offers a distinct end-to-end network for attention-based self-governing f...
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In recent years, spiking neural networks (SNNs) have gained significant attention in visual recognition tasks due to the low computational energy. However, most SNNs have a large number of parameters, which limits the...
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Nowadays, Emotion Recognition plays a key role in social communication interaction in which human emotions are interpreted from facial expressions and speech. Several challenges occur in emotion recognition such as ca...
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The rapid advancement of computer image production technology presents a grave peril to the trustworthiness of digital images, necessitating a great practical requirement for research on computer generated image detec...
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This article explores the use of classical machine learning and Automnl methods to solve the problem of camera identification from photo images in the absence of metadata and limited computational and resource capabil...
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To enhance the weakness of local binary pattern (LBP) and its state-of-the-art variants, this letter presents a new variant of the local concave microstructure pattern (LCvMSP). The proposed multi-scale shape index ba...
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To enhance the weakness of local binary pattern (LBP) and its state-of-the-art variants, this letter presents a new variant of the local concave microstructure pattern (LCvMSP). The proposed multi-scale shape index based texture descriptor is named as SI-LCvMSP. Contrarily to the original LBP and LCvMSP, SI-LCvMSP uses the shape index instead of the original texture image in the kernel function. The shape index is a differential calculation and it can be calculated from local second-order derivatives of texture images. It captures microstructure and macrostructure texture information mathematically. As textural features, we use multi-scale and multi-resolution shape index information as well as rotation-invariant uniform LBP. Thus, we obtain the discriminative feature representation schema to construct cross-scale joint coding. The proposed method has a high discriminability and is less sensitive to image transforms such as rotation and illumination. Experimental results show that the SI-LCvMSP descriptor can improve classification accuracy.
One-dimensional local binary pattern (1DLBP) has been recently specialized for feature extraction from different types of 1D biological signals. One of the major drawbacks of using 1DLBP, which unavoidably results in ...
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One-dimensional local binary pattern (1DLBP) has been recently specialized for feature extraction from different types of 1D biological signals. One of the major drawbacks of using 1DLBP, which unavoidably results in classification accuracy reduction, is its noise sensitivity due to the thresholding mechanism. To overcome this deficiency, we have proposed a new one-dimensional noise-tolerant binarypattern (1DNTBP) in this paper. In contrast to 1DLBP, our proposed operator has been defined to use information of a sampling interval as a threshold instead of using central sample value. In order to evaluate 1DNTBP, we applied our proposed feature extraction method on sEMG for basic hand movement dataset. Additionally, a feature selection stage has been considered to perform further noise removal and insignificant patterns reduction process. Hereafter, a variety of classifiers have been tested with the aim of categorizing the selected features. Experimental results indicate that not only does the proposed operator provide noise tolerance, but also it works adaptably well with various classifiers causing it to be a universal operator, sufficiently appropriate to be applied to different applications.
The global pandemic of novel coronavirus that started in 2019 has ser-iously affected daily lives and placed everyone in a panic *** coronavirus led to the adoption of social distancing and people avoiding unneces-sar...
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The global pandemic of novel coronavirus that started in 2019 has ser-iously affected daily lives and placed everyone in a panic *** coronavirus led to the adoption of social distancing and people avoiding unneces-sary physical contact with each *** present situation advocates the require-ment of a contactless biometric system that could be used in future authentication systems which makesfingerprint-based person identification ***-lar biometric is the solution because it does not require physical contact and is able to identify people wearing face ***,the periocular biometric region is a small area,and extraction of the required feature is the point of *** paper has proposed adopted multiple features and emphasis on the periocular *** the proposed approach,combination of local binary pattern(LBP),color histogram and features in frequency domain have been used with deep learning algorithms for classifi***,we extract three types of fea-tures for the classification of periocular regions for *** LBP represents the textual features of the iris while the color histogram represents the frequencies of pixel values in the RGB *** order to extract the frequency domain fea-tures,the wavelet transformation is *** learning from these features,a convolutional neural network(CNN)becomes able to discriminate the features and can provide better recognition *** proposed approach achieved the highest accuracy rates with the lowest false person identification.
Recognizing land type using remote sensing images is vital for land resource management and monitoring. To improve the accuracy of land scene recognition from remote sensing images captured by sensors, an innovative c...
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Recognizing land type using remote sensing images is vital for land resource management and monitoring. To improve the accuracy of land scene recognition from remote sensing images captured by sensors, an innovative classification method based on an improved capsule network was proposed. First, a circular local binary pattern algorithm extracted texture features from remote sensing images. The textured images were then fused with the original remote sensing images, and a 1-by-1 convolutional layer combined the fused feature graphs linearly. Finally, an improved capsule network performed remote sensing image scene classification. The experimental results show that the average accuracy rate of the proposed method improved by more than 6% compared with that of the typical convolutional neural network method, and the proposed approach outperformed other comparable methods in terms of accuracy. The proposed method not only provides a fast convergence speed but also improves the scene classification accuracy for remote sensing images, especially for images with rich texture information. This is helpful for land remote sensing image scene classification in various applications. (c) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)
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