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2018 | OriginalPaper | Buchkapitel

Utilizing Deep Learning and 3DLBP for 3D Face Recognition

verfasst von : João Baptista Cardia Neto, Aparecido Nilceu Marana

Erschienen in: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

Verlag: Springer International Publishing

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Abstract

Methods based on biometrics can help prevent frauds and do personal identification in day-to-day activities. Automated Face Recognition is one of the most popular research subjects since it has several important properties, such as universality, acceptability, low costs, and covert identification. In constrained environments methods based on 2D features can outperform the human capacity for face recognition but, once occlusion and other types of challenges are presented, the aforementioned methods do not perform so well. To deal with such problems 3D data and deep learning based methods can be a solution. In this paper we propose the utilization of Convolutional Neural Networks (CNN) with low-level 3D local features (3DLBP) for face recognition. The 3D local features are extracted from depth maps captured by a Kinect sensor. Experimental results on Eurecom database show that this proposal is promising, since, in average, almost 90% of the faces were correctly recognized.

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Vorheriges Kapitel DNLM-IIFFT: An Implementation of the Deceived Non Local Means Filter Using Integral Images and the Fast Fourier Transform for a Reduced Computational Cost

Nächstes Kapitel Efficient Hyperparameter Optimization in Convolutional Neural Networks by Learning Curves Prediction

https://developer.microsoft.com/en-us/windows/kinect.

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Titel: Utilizing Deep Learning and 3DLBP for 3D Face Recognition
verfasst von: João Baptista Cardia Neto
Aparecido Nilceu Marana
Verlag: Springer International Publishing
Buch: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Print ISBN: 978-3-319-75192-4

Electronic ISBN: 978-3-319-75193-1

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-75193-1_17

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