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8 October 2018 Comparative study of deep learning and classical methods: smart camera implementation for face authentication
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Abstract
In this work, we investigate biometrics applied on 2D faces in order to secure areas requiring high security level. Based on emerging deep learning methods (more precisely transfer learning) as well as two classical machine learning techniques (Support Vector Machines and Random Forest), different approaches have been used to perform person authentication. Preprocessing filtering steps of input images have been included before features extraction and selection. The goal has been to compare those in terms of processing time, storage size and authentication accuracy according to the number of input images (for the learning task) and preprocessing tasks. We focus on data-related aspects to store biometric information on a low storage capacity remote card (10Ko), not only in a high security context but also in terms of privacy control. The proposed solutions guarantee users the control of their own biometrics data. The study highlights the impact of preprocessing to perform real-time computation, preserving a relevant accuracy while reducing the amount of biometric data. Considering application constraints, this study concludes with a discussion dealing with the tradeoff of the available resources against the required performances to determine the most appropriate method.
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© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Pierre Bonazza, Johel Miteran, Dominique Ginhac, and Julien Dubois "Comparative study of deep learning and classical methods: smart camera implementation for face authentication", Proc. SPIE 10802, Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies II, 108020I (8 October 2018); https://doi.org/10.1117/12.2325697
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