A novel neuroscience-inspired architecture: For computer vision applications

Hassan, Marwa Yousif; Khalifa, Othman O.; Talib, Azhar Bin Abu; Olanrewaju, Rashidah Funke; Abdalla, Aisha H.

doi:10.1109/sgcac.2016.7458013

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…However, to date, there are many unresolved issues in this sphere -how to teach a computer to conduct the recognition procedure according to various evaluation criteria; how to decode and store digital face images using the least amount of memory; how to select effective criteria to evaluate the similarity between faces; how to conduct a comprehensive image processing (Lin et al, 2000). The algorithms of this class must meet the following basic requirements: high recognition quality, real-time operation and robustness against external factors (Hassan et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Development of a Computer System for Identity Authentication Using Artificial Neural Networks

et al. 2017

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The aim of the study is to increase the effectiveness of automated face recognition to authenticate identity, considering features of change of the face parameters over time. The improvement of the recognition accuracy, as well as consideration of the features of temporal changes in a human face can be based on the methodology of artificial neural networks. Hybrid neural networks, combining the advantages of classical neural networks and fuzzy logic systems, allow using the network learnability along with the explanation of the findings. The structural scheme of intelligent system for identification based on artificial neural networks is proposed in this work. It realizes the principles of digital information processing and identity recognition taking into account the forecast of key characteristics' changes over time (e.g., due to aging). The structural scheme has a three-tier architecture and implements preliminary processing, recognition and identification of images obtained as a result of monitoring. On the basis of expert knowledge, the fuzzy base of products is designed. It allows assessing possible changes in key characteristics, used to authenticate identity based on the image. To take this possibility into consideration, a neuro-fuzzy network of ANFIS type was used, which implements the algorithm of Tagaki-Sugeno. The conducted experiments showed high efficiency of the developed neural network and a low value of learning errors, which allows recommending this approach for practical implementation. Application of the developed system of fuzzy production rules that allow predicting changes in individuals over time, will improve the recognition accuracy, reduce the number of authentication failures and improve the efficiency of information processing and decision-making in applications, such as authentication of bank customers, users of mobile applications, or in video monitoring systems of sensitive sites.

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Section: Introductionmentioning

confidence: 99%

Development of a Computer System for Identity Authentication Using Artificial Neural Networks

et al. 2017

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“…Feature Parallelism Model [4] has addressed the parallel nature of the brain. It conceptualizes underutilized facts about the human visual system; namely, the Feature Integration Theory of visual attention "FIT" [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%