Traditional Machine Learning or Deep Learning Methods for Embedded Computer Vision Study on Biometric Application

Pierre Bonazza, Johel Miteran, Dominique Ginhac and Julien Dubois
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Julien Dubois: Universite de Bourgogne Franche-Comte, France

Biostatistics and Biometrics Open Access Journal, 2019, vol. 9, issue 1, 28-30

Abstract: Today, Convolutional Neural Networks (CNN) provide one of the best performances in state of the art image recognition, while requiring high computational power and being very time consuming. So it raises the following questions: can deep learning achieve better performances than machine learning over different computer vision aspect, such as development time, processing time, raw performances or resource consumption? Considering strong constraints on this listing, can we still consider Machine Learning for a specific computer vision application? Hence, the purpose of this paper is to provide an insight into these questions by comparing methods from both Deep Learning and traditional Machine Learning, applied on a real-time person authentication application, using 2D faces under a binary classification problem. These methods are embedded in a restricted power computational unit, forming a smart camera composing a low-cost security system. This application needs the biometric model to be minimized so it can be stored on a remote personal media (10 KB).

Keywords: Biometrics Open Access Journal; Biostatistics and Biometrics; Biostatistics and Biometrics Open Access Journal; Open Access Journals; biometrics journal; biometrics articles; biometrics journal reference; biometrics journal impact factor; biometrics and biostatistics journal impact factor; journal of biometrics; open access juniper publishers; juniper publishers reivew (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:adp:jbboaj:v:9:y:2019:i:1:p:28-30

DOI: 10.19080/BBOAJ.2019.09.555755

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