An Extensive Study on Traditional-to-Recent Transformation on Face Recognition System

Juneja, Kapil; Rana, Chhavi

doi:10.1007/s11277-021-08170-3

Cited by 21 publications

(10 citation statements)

References 166 publications

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“…Increasing concerns over the privacy of individuals in the video surveillance system led to the proposal of many solutions. Fog computing has also emerged as a practical solution that extends the capabilities of cloud concerning the end devices [6][7][8][9]. In this section we review some of the privacy preservation techniques proposed for video surveillance.…”

Section: Literature Surveymentioning

confidence: 99%

Smart Fog-Based Video Surveillance with Privacy Preservation based on Blockchain

Dave

Rastogi

Miglani

et al. 2021

Wireless Pers Commun

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Security surveillance of home or office premises is usually performed by deploying a number of video cameras to continuously monitor the environment. Such monitoring has a potential to cause serious violation of privacy of individuals or individual rights as their movements are continuously observed. The existing centralised security systems can also be misused to collect the personal information for example the collected information could be used to launch cyber frauds using collected biometric identities. To address these issues, in this paper a distributed edge-fog node based video surveillance system is proposed for smart home environments for privacy preservation of individuals. The proposed system is event driven and resource efficient as it utilizes motion detection to detect intrusions and filters unnecessary data in the surveillance system. To meet users' privacy protection demands, a reversible blurring is performed on the privacy sensitive objects detected in the captured video stream. The proposed solution consumes less resources and provides better privacy preserving functionality. The system is coupled with a private Blockchain network that integrates into the surveillance system. This transformation of the surveillance system can be used to check and maintain integrity, management of blurring keys, and provide authorization rights to access video data.

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Section: Literature Surveymentioning

confidence: 99%

Smart Fog-Based Video Surveillance with Privacy Preservation based on Blockchain

Dave

Rastogi

Miglani

et al. 2021

Wireless Pers Commun

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“…The majority of existing real-world biometric systems are based on unimodal biometric recognition, which makes use of a single biometric modality and needs to be accurately enrolled in database for training the algorithm, then needs to be sufficiently acceptable and usable in recaptured probe or test samples for achieving successful recognition. Thus, such biometric systems may still suffer from several limitations, especially with unexpected or uncontrolled test or query data used to probe the biometric system, such as occlusions, variations, noise, and low quality [2,3].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Feature Subset Selection for Occluded Face Recognition

Alsaedi¹,

Jaha²

2022

Intelligent Automation &Amp; Soft Computing

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Accurate recognition of person identity is a critical task in civil society for various application and different needs. There are different well-established biometric modalities that can be used for recognition purposes such as face, voice, fingerprint, iris, etc. Recently, face images have been widely used for person recognition, since the human face is the most natural and user-friendly recognition method. However, in real-life applications, some factors may degrade the recognition performance, such as partial face occlusion, poses, illumination conditions, facial expressions, etc. In this paper, we propose two dynamic feature subset selection (DFSS) methods to achieve better recognition for occluded faces. The proposed DFSS methods are based on resilient algorithms attempting to minimize the negative influence of confusing and low-quality features extracted from occluded areas by either exclusion or weight reduction. Principal Component Analysis and Gabor filtering based approaches are initially used for face feature extraction, then the proposed DFSS methods are dynamically enforced. This is leading to more effective feature representation and an improved recognition performance. To validate their effectiveness, multiple experiments are conducted and the performance of different methods is compared. The experimental work is carried out using AR database and Extended Yale Face Database B. The obtained results of face identification and verification show that both proposed DFSS methods outperform the standard (static) use of the whole number of features and the equal feature weights.

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“…People have conducted in-depth and extensive research on various stages of face recognition, including face feature capture, feature generation, face correction, etc. (Juneja & Rana, 2021).…”

Section: Introductionmentioning

confidence: 99%

Research on Microvideo Character Perception and Recognition Based on Target Detection Technology

Yue¹

2022

JCCE

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With the rapid development of the Internet short-video platform, microvideo is increasingly attracting users because of its small traffic and time period. However, the content of microvideo on the Internet is complex, with illegal information. This research is an improved algorithm based on target detection technology, aiming at the characteristics of difficult character recognition in microvideo. The algorithm combines Gabor wavelet transform algorithm and 2D principal component analysis (PCA) algorithm and makes full use of video perception technology to realize character recognition in microvideo. Through MATLAB simulation analysis, it can be seen that the accuracy of the optimization algorithm proposed in this paper is 86.34% and the recognition time is 34.28 s. Compared with the traditional PCA algorithm and artificial neural network (ANN) algorithm, the optimization algorithm proposed in this paper has better recognition rate and recognition efficiency.

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An Extensive Study on Traditional-to-Recent Transformation on Face Recognition System

Cited by 21 publications

References 166 publications

Smart Fog-Based Video Surveillance with Privacy Preservation based on Blockchain

Smart Fog-Based Video Surveillance with Privacy Preservation based on Blockchain

Dynamic Feature Subset Selection for Occluded Face Recognition

Research on Microvideo Character Perception and Recognition Based on Target Detection Technology

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