RETRACTED: An effective face recognition system based on Cloud based IoT with a deep learning model

Chauhan, Deepika; Kumar, Ashok; Bedi, Pradeep; Athavale, Vijay Anant; Veeraiah, D.; Prathap, Boppuru Rudra

doi:10.1016/j.micpro.2020.103726

Cited by 26 publications

(10 citation statements)

References 20 publications

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“…A Face Recognition system based on Cloud Authentication (FRCA) was developed [12] using Tree-based Deep Neural Network (TDNN) for automated face verification in a cloud. The tree was characterized by its limb length and weight with remaining ability consisting of a two-stage layer, array game plan and indirect competence.…”

Section: Related Workmentioning

confidence: 99%

Dwindling DCNN Training Time and Improving Authentication by Spurring Algorithm in Cloud Computing

2024

IJIES

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Cloud Computing (CC) is a solution that efficiently maps cloud tasks to resources, ensuring highperformance utilization in computing. But, the challenge lies in managing attacks while managing a large data flow. For this reason, Lightweight deep learning model to secure authentication (LDLSA) was developed using Deep Convolutional Neural Network (DCNN) and Homomorphic Encryption (HE) is developed for deep face recognition authentication, enhancing privacy protection in CC. However, these models are computationally expensive, require extensive training data and lack information on cloud-stored authentication data for privacy preservation. In this paper, DL with Cloud Authentication (dCAuth) is proposed to resolve the above mentioned issues to provide efficient cloud authentication with lesser complexity. This method employs a spurring algorithm for dwindling DCNN with recall (dDCNNr) to handle the number of parameters in DCNN to handle a large number of parameters for cloud authentication. dDCNNr model reduces execution time and improves classification accuracy, but it necessitates faster training time. To solve this, Fully Connected (FC) layers of DCNN is replaced with Hopfield Neural Network (HNN). The model utilises a DCNN for feature extraction and HNN for pattern recognition highly focusing on authentication, both the process have similar training phase with known patterns by adjusted weights. The proposed authentication method stores encrypted Neural Network (NN) weights in the cloud, eliminating the need for a verification table. The weights for unknown input patterns are generated during pattern recall. The HNN weight matches input indicating a known or legal pattern, aiming to identify a known pattern that best fits the input with minimum processing resources. The proposed authentication model integrates DCNN and HNN to rapidly and precisely recall the legitimate user ID and face image (password) information. The model effectively manages the large training data and parameters, ensuring privacy for cloud-stored authentication data, simultaneously reducing the time duration of registration and password changes (old to new face image). Finally, an extensive simulation reveals that the proposed model achieves accuracy of 94.15%, 94.53% and 94.38% on Georgia Tech face (GTF), Labelled Faces in the Wild (LFW) and Biometric Signature (BS) database respectively.

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Section: Related Workmentioning

confidence: 99%

Dwindling DCNN Training Time and Improving Authentication by Spurring Algorithm in Cloud Computing

2024

IJIES

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“…A tree-based deep model (Chauhan et al, 2021) was developed for recognizing the facial features in the cloud system. An extra dimension was separated into a small portion and a stick was prepared for all portions.…”

Section: Literature Surveymentioning

confidence: 99%

Efficient Handling of Medical Data Classification in Cloud-Edge Network using Optimization Algorithm

Saranya¹,

Fatima²

2021

Journal of Computer Science

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Nowadays, a cloud-edge computing framework with IoT offers different medicinal facilities by classifying a massive amount of patients' health data through a Deep Neural Network. But, how to optimize task scheduling while carrying multiple tasks from multiple edge devices in realtime was still challenging. This article introduces a cooperative cloud-edge computing structure to effectively perform the fuzzy DNN classification into the edge system and handle the computationally complicated tasks of DNNs. First, the edge servers are constructed with fuzzy DNNs and cooperate with the cloud to create a cooperative cloud-edge computing paradigm. Then, an adaptive deployment method is developed using a Lion Optimization Algorithm, which supports the cloud to decide which task will be executed at the edge devices. Therefore, the study of fuzzy DNN using health data is performed for forecasting and diagnosing various diseases. Finally, the simulation outcomes reveal that the LOA achieved 37.8Jin energy use and 17.8ms latency while using 25 edge devices. Also, the fuzzy DNN achieved 85.8% accuracy for classifying the medical data and diagnosing them in the earlier stage. It concludes that LOA and fuzzy DNN are more efficient than classical optimization and classification for healthcare applications using the cloud-edge computing paradigm.

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“…The development of the Internet of Things has changed people's thinking, which is reflected in the combination of it with sports training, which has opened up a new path for the intelligentization of training [1]. According to the characteristics of current sports training, it is of great significance to accurately identify the movement characteristics of athletes and judge whether they are standardized or not for formulating training programs and improving training efficiency [2]. At this stage, most of sports action detection relies on sensors to complete, and there are four detection methods, namely mechanical, electromagnetic, acoustic and optical.…”

Section: Introductionmentioning

confidence: 99%

Application of Low Resolution Face Recognition Based on Infrared Spectral Imaging in Motion Training Action Analysis

Jinglong

2024

Preprint

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As a conscious activity, the importance of sports in social life is self-evident. However, how to use modern scientific and technological means to improve sports fun and athletes' competitive ability has become the current research hotspot. The current sports detection system has a high restriction on Athletes' movements. Therefore, it is an inevitable direction of future development to integrate intelligent technology into sports movement detection, and face recognition technology has become the best choice with its unique technical advantages. This paper constructs an application system that combines the low-resolution face recognition technology of the Internet of things with sports training action analysis. The system determines the design idea of the system framework by optimizing the face recognition model and the embedded algorithm, and the Softmax loss function is applied to the system to optimize the recognition accuracy and credibility. In addition, the system determines the parameter response value by analyzing the elbow and knee joints in the video. Finally, according to the test of system reliability, it is found that the system with GPU as the processor has the lowest resource occupancy and the highest reliability, which indicates that the system not only avoids the problem of low accuracy of traditional identification equipment, but also makes the action detection result more reliable.

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RETRACTED: An effective face recognition system based on Cloud based IoT with a deep learning model

Cited by 26 publications

References 20 publications

Dwindling DCNN Training Time and Improving Authentication by Spurring Algorithm in Cloud Computing

Dwindling DCNN Training Time and Improving Authentication by Spurring Algorithm in Cloud Computing

Efficient Handling of Medical Data Classification in Cloud-Edge Network using Optimization Algorithm

Application of Low Resolution Face Recognition Based on Infrared Spectral Imaging in Motion Training Action Analysis

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