Enhanced long-range personal identification based on multimodal information of human features

Tsai, Hsin‐Chun; Chen, Bo-Wei; Wang, Jhing-Fa; Paul, Anand

doi:10.1007/s11042-013-1606-6

Cited by 5 publications

(5 citation statements)

References 22 publications

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“…As a source of statistical information, we used open data posted on the website of the Federal Service for Technical and Export Control of Russia [15]. To build a theoretical model for reducing the redundancy of personal data when processed by AI, the most appropriate method for working with indirect and inaccurate data would be to conduct a mental experiment using a statistical method of technical diagnostics based on the generalized Bayesian formula, which allows the probability of an event in the presence of indirect and inaccurate data that must be determined [5], [6], [16]- [18].…”

Section: Methodsmentioning

confidence: 99%

“…In this connection, the authors proposed to use a common formal basis for the analysis and comparison of communication protocols. Determining the requirements for the security of personal data for the entire life cycle of technologies is a key requirement [5], and the Bayesian methodology [24] and the method differentiated confidentiality [25] were studied for the analysis of personal data in the social network [26], [27].…”

Section: Related Workmentioning

confidence: 99%

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The Bayes model for the protection of human interests

Zharova,

Elin,

Levashov

2023

IJECE

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<span lang="EN-US">This article is aimed at solving a number of issues related to the problems, risks, and threats arising from the profiling of human activity. In this study, the Bayesian method was used, to determine the quantitative and qualitative characteristics of personal data for ensuring the security of this data by dint of reducing the redundancy of data processed by artificial intelligence (AI). A thought experiment to test the possibility of reducing the redundancy of personal data processed by AI allows us to conclude that using the Bayesian method allows to protect human rights to privacy. With this approach, instead of the method associated with the collection and accumulation of the most sensitive categories of personal data, we proposed a method that is associated with obtaining probabilistic estimates of the values of the parameters of these data by conducting statistical studies of the specified personal data without their collection and accumulation. The probabilistic estimates of the parameters of some sensitive personal data obtained in this way can replace their exact values and can be used by AI in the criteria for filtering personal data subjects, including for the purpose of making a decision.</span>

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

The Bayes model for the protection of human interests

Zharova,

Elin,

Levashov

2023

IJECE

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“…A generic scheme can be designed while keeping a balance relation between user preferences and network centric approaches. The QoS of a handover scheme can be enhanced while designing a scheme which requires less amount of data for fetching multimedia information to the MN during handover process [7,8]. Nowadays Internet of Things (IoT) and Cyber Physical System (CPS) are evolving very rapidly.…”

Section: Introductionmentioning

confidence: 99%

An Optimized Network Selection and Handover Triggering Scheme for Heterogeneous Self-Organized Wireless Networks

Khan

Han

2014

Mathematical Problems in Engineering

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Optimizing the balance between different handover parameters for network selection is one of the challenging tasks for seamless communication in heterogeneous networks. Traditional approaches for network selection are mostly based on the Receive Signal Strength (RSS) from the Point of Attachment (PoA) of a network. Most of these schemes are suffered from high handover delay, false handover indications, and inappropriate network selection for handover. To address these problems, we present an optimized network selection scheme based on the speed of a mobile node. A mechanism based on two different thresholds on the speed of a Mobile Node (MN) is integrated in the proposed scheme. If the speed of an MN is greater than any of the threshold, the MN performs handover to a particular network. We employ Grey Relational Analysis (GRA) in the proposed scheme to select the best PoA of the selected network. Similarly, to deal with false handover indications, we proposed an optimized handover triggering technique. We compare our proposed scheme with existing schemes in context of energy consumption for scanning, frequent and failed handovers, packet loss ratio, and handover delay. The proposed scheme shows superior performance and it outperforms existing schemes used for similar purpose. Moreover, simulation results show the accuracy and performance of the proposed scheme.

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“…This special issue covers a wide range of topics in human-machine interaction, including text-or speech-driven facial animation [11,23,28], emphatic speech synthesis [20], head and facial gesture synthesis [10], human pose estimation [24], crowd counting [6], person identification [25] and facial expression recognition [8].…”

mentioning

confidence: 99%

“…They propose a scene-adaptive, accurate and fast crowd counting approach using joint depth and color information. Tsai et al [25] address the problem of long-range person identification with a multimodal information fusion approach, which includes multiview face detection, height measurement and face recognition. Recognizing human facial expressions is critical to NUI applications that are required to understand human emotions.…”

mentioning

confidence: 99%

Multimodal joint information processing in human machine interaction: recent advances

Xie

Deng

Cox

2013

Multimed Tools Appl

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Interaction between humans is a multimodal process in nature, which integrates the different modalities of vision, hearing, gesture and touch. It is not surprising then that conventional uni-modal human-machine interactions lag in performance, robustness and naturalness when compared with human-human interactions. Recently, there has been increasing research interest in jointly processing information in multiple modalities and mimicking human-human multimodal interactions [2,4,5,9,13,14,16,18,19,21,22]. For example, human speech production and perception are bimodal in nature: visual cues have a broad influence on perceived auditory stimuli [17]. The latest research in speech processing has shown that integration of both auditory and visual information, i.e., acoustic speech combined with facial and lip motions, achieves significant performance improvement in tasks such as speech recognition [3] and emotion recognition [16]. However, information from difference modalities can be difficult to integrate: the representations of different signals are heterogeneous and the signals are often asynchronous and loosely-coupled. Therefore, finding more effective ways to integrate and jointly process information from different modalities is essential for the success of such multimodal human-machine interactive systems. Meanwhile, multimodal applications are becoming increasingly important, especially for mobile computing and digital entertainment scenarios, such as natural user interfaces (NUI) on smartphones and motion sensing gaming.This special issue aims to bring together work by researchers and technologists engaged in the development of multimodal technologies for information processing, emerging multimedia applications and user-centric human computer interaction. We received more than L. Xie ( )

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Enhanced long-range personal identification based on multimodal information of human features

Cited by 5 publications

References 22 publications

The Bayes model for the protection of human interests

The Bayes model for the protection of human interests

An Optimized Network Selection and Handover Triggering Scheme for Heterogeneous Self-Organized Wireless Networks

Multimodal joint information processing in human machine interaction: recent advances

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