A Novel Forgery Detection in Image Frames of the Videos
Using Enhanced Convolutional Neural Network in Face Images

Velliangiri, S.; Premalatha, J.

doi:10.32604/cmes.2020.010869

Cited by 12 publications

(3 citation statements)

References 18 publications

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“…Background subtraction (BGS) serves as the primary and crucial stage for human detection in image, and it is utilized further for feature extraction purposes. Several BGS techniques have been suggested previously, such as self-organizing maps, statistical methods, temporal, and feature-based methods [21]. However, BGS methods usually rely on color spaces, such as RGB, YUV, and HSV that include limitations of color camouflage and lighting changes.…”

Section: Rgb-d Image's Silhouette Extraction Using Depth Silhouettesmentioning

confidence: 99%

Diagnosis of Neurological Disorders Rehabilitation Using Effective Feature Elimination and LSTM-RNN Classifier

Batool,

Jalal,

Alazeb

et al. 2023

Preprint

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Neurological disorders affect bodily functions and there is a growing need for advanced solutions to offer therapeutic advice to patients. A framework using artificial intelligence and machine learning techniques has been proposed to address this. The proposed system employs a combination of RGB, inertial, and depth sensors data. The inertial signals have been filtered using a notch filter to obtain the optimal wearable sensor data by examining the upper and lower cutoff frequencies. Multiple features have been calculated, including mel frequency cepstral coefficients (MFCC), statistical features and Gaussian mixture model (GMM) features. On the other hand, silhouettes have been extracted from RGB and depth images, and four crucial parameters have been employed to gauge the level of accuracy with which patients with neurological disorders performed their activities, including the angle formation between the hands and lower/upper half of the body and center of the body, and the angle between the hands of the silhouette. The resulting features have been fused and classified using principle component analysis and a reweighted genetic algorithm. Evaluation using cross-validation on mRI (multi-modal 3d human pose estimation dataset using mmwave, RGB-D, and inertial sensors) and MHEALTH (mobile health) datasets showed a recognition accuracy rate of 97.29\% and a 97.94\%. The study highlights the need for more datasets to address challenges in rehabilitation using human activity recognition with multi-modal sensors.

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Section: Rgb-d Image's Silhouette Extraction Using Depth Silhouettesmentioning

confidence: 99%

Diagnosis of Neurological Disorders Rehabilitation Using Effective Feature Elimination and LSTM-RNN Classifier

Batool,

Jalal,

Alazeb

et al. 2023

Preprint

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“…It was proved that the accuracy of the gesture analysis of this model was 11.6~24.0% higher than that of the machine learning model. Velliangiri et al [13] designed an automatic detection model that combined CNN and crow search algorithms to solve false-frame recognition in video images. The test results showed that the method had a false-frame-recognition accuracy of 97.21% on the experimental dataset, which was higher than the other existing methods.…”

Section: Related Workmentioning

confidence: 99%

The Application of the Gesture Analysis Method Based on Hybrid RF and CNN Algorithms in an IoT–VR Human–Computer Interaction System

2023

Processes

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With the development of the Internet of Things (IoT) and virtual reality (VR) technology, the demand for high-precision gesture intelligent analysis of a human–machine interaction module for IoT–VR systems is increasing. Therefore, random forest (RF) and convolution neural network (CNN) algorithms are used in this study to build an intelligent gesture recognition model. The experiments were conducted to test the application performance of the design model. The test results show that the qualification rate of the analytical model designed in this study is significantly higher than that of the comparative model. When the threshold is determined to be 43.26 mm, the analytical qualification rates of the RF-CNN (the method of combining RF with CNN algorithms), faster regions with CNN features (Faster-RCNN), and RF models are 82.41%, 76.10%, and 59.10%, respectively. The calculation time of the RF–CNN model is between the two comparative models. From the test data, it can be observed that the research results have certain significance for improving the accuracy of gesture machine recognition technology in China’s VR Internet of Things (IoT) system.

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“…This step recognizes the Braille cells based on an end-to-end DCNN model due to its ability to learn image features better than traditional methods for image feature extraction [25][26][27][28]. Moreover, DCNN models have achieved high accuracy results compared with other deep learning models for several computer vision-based tasks [29].…”

Section: Braille Cell Recognitionmentioning

confidence: 99%

A Deep Learning-Based Recognition Approach for the Conversion of Multilingual Braille Images

Al-Salman¹,

Gumaei²,

AlSalman³

et al. 2021

Computers, Materials &Amp; Continua

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Braille-assistive technologies have helped blind people to write, read, learn, and communicate with sighted individuals for many years. These technologies enable blind people to engage with society and help break down communication barriers in their lives. The Optical Braille Recognition (OBR) system is one example of these technologies. It plays an important role in facilitating communication between sighted and blind people and assists sighted individuals in the reading and understanding of the documents of Braille cells. However, a clear gap exists in current OBR systems regarding asymmetric multilingual conversion of Braille documents. Few systems allow sighted people to read and understand Braille documents for self-learning applications. In this study, we propose a deep learning-based approach to convert Braille images into multilingual texts. This is achieved through a set of effective steps that start with image acquisition and preprocessing and end with a Braille multilingual mapping step. We develop a deep convolutional neural network (DCNN) model that takes its inputs from the second step of the approach for recognizing Braille cells. Several experiments are conducted on two datasets of Braille images to evaluate the performance of the DCNN model. The rst dataset contains 1,404 labeled images of 27 Braille symbols representing the alphabet characters. The second dataset consists of 5,420 labeled images of 37 Braille symbols that represent alphabet characters, numbers, and punctuation. The proposed model achieved a classi cation accuracy of 99.28% on the test set of the rst dataset and 98.99% on the test set of the second dataset. These results con rm the applicability of the DCNN model used in our proposed approach for multilingual Braille conversion in communicating with sighted people.

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A Novel Forgery Detection in Image Frames of the Videos Using Enhanced Convolutional Neural Network in Face Images

Cited by 12 publications

References 18 publications

Diagnosis of Neurological Disorders Rehabilitation Using Effective Feature Elimination and LSTM-RNN Classifier

Diagnosis of Neurological Disorders Rehabilitation Using Effective Feature Elimination and LSTM-RNN Classifier

The Application of the Gesture Analysis Method Based on Hybrid RF and CNN Algorithms in an IoT–VR Human–Computer Interaction System

A Deep Learning-Based Recognition Approach for the Conversion of Multilingual Braille Images

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