Depth-Based Face Recognition by Learning from 3D-LBP Images (Unknown language)

How to get this document?

Commercial Copyright fee: €14.50 Basic fee: €4.00 Total price: €18.50
Academic Copyright fee: €4.50 Basic fee: €2.00 Total price: €6.50

In this paper, we propose a hybrid framework for face recognition from depth images, which is both effective and efficient. It consists of two main stages: First, the 3DLBP operator is applied to the raw depth data of the face, and used to build the corresponding descriptor images (DIs). However, such operator quantizes relative depth differences over/under +-7 to the same bin, so as to generate a fixed dimensional descriptor. To account for this behavior, we also propose a modification of the traditional operator that encodes depth differences using a sigmoid function. Then, a not-so-deep (shallow) convolutional neural network (SCNN) has been designed that learns from the DIs. This architecture showed two main advantages over the direct application of deep-CNN (DCNN) to depth images of the face: On the one hand, the DIs are capable of enriching the raw depth data, emphasizing relevant traits of the face, while reducing their acquisition noise. This resulted decisive in improving the learning capability of the network; On the other, the DIs capture low-level features of the face, thus playing the role for the SCNN as the first layers do in a DCNN architecture. In this way, the SCNN we have designed has much less layers and can be trained more easily and faster. Extensive experiments on low- and high-resolution depth face datasets confirmed us the above advantages, showing results that are comparable or superior to the state-of-the-art, using by far less training data, time, and memory occupancy of the network.

Table of contents conference proceedings

The tables of contents are generated automatically and are based on the data records of the individual contributions available in the index of the TIB portal. The display of the Tables of Contents may therefore be incomplete.

POP: Full Parametric model Estimation for Occluded People
Marin, Riccardo / Melzi, Simone / Mitra, Niloy J. / Castellani, Umberto | 2019
mpLBP: An Extension of the Local Binary Pattern to Surfaces based on an Efficient Coding of the Point Neighbours
Thompson, Elia Moscoso / Biasotti, Silvia / Digne, Julie / Chaine, Raphaelle | 2019
Sketch-Aided Retrieval of Incomplete 3D Cultural Heritage Objects
Lengauer, Stefan / Komar, Alexander / Labrada, Arniel / Karl, Stephan / Trinkl, Elisabeth / Preiner, Reinhold / Bustos, Benjamin / Schreck, Tobias | 2019
Protein Shape Retrieval Contest
Langenfeld, Florent / Axenopoulos, Apostolos / Benhabiles, Halim / Daras, Petros / Giachetti, Andrea / Han, Xusi / Hammoudi, Karim / Kihara, Daisuke / Lai, Tuan M. / Liu, Haiguang et al. | 2019
Extended 2D Scene Sketch-Based 3D Scene Retrieval
Yuan, Juefei / Abdul-Rashid, Hameed / Li, Bo / Lu, Yijuan / Schreck, Tobias / Bui, Ngoc-Minh / Do, Trong-Le / Nguyen, Khac-Tuan / Nguyen, Thanh-An / Nguyen, Vinh-Tiep et al. | 2019
Extended 2D Scene Image-Based 3D Scene Retrieval
Abdul-Rashid, Hameed / Yuan, Juefei / Li, Bo / Lu, Yijuan / Schreck, Tobias / Bui, Ngoc-Minh / Do, Trong-Le / Holenderski, Mike / Jarnikov, Dmitri / Le, Khiem T. et al. | 2019
Classification in Cryo-Electron Tomograms
Gubins, Ilja / Schot, Gijs van der / Veltkamp, Remco C. / Förster, Friedrich / Du, Xuefeng / Zeng, Xiangrui / Zhu, Zhenxi / Chang, Lufan / Xu, Min / Moebel, Emmanuel et al. | 2019
Depth-Based Face Recognition by Learning from 3D-LBP Images
Neto, Joao Baptista Cardia / Marana, Aparecido Nilceu / Ferrari, Claudio / Berretti, Stefano / Bimbo, Alberto Del | 2019
CMH: Coordinates Manifold Harmonics for Functional Remeshing
Marin, Riccardo / Melzi, Simone / Musoni, Pietro / Bardon, Filippo / Tarini, Marco / Castellani, Umberto | 2019
Generalizing Discrete Convolutions for Unstructured Point Clouds
Boulch, Alexandre | 2019
A 3D CAD Assembly Benchmark
Lupinetti, Katia / Giannini, Franca / Monti, Marina / Pernot, Jean-Philippe | 2019
Feature Curve Extraction on Triangle Meshes
Thompson, E. Moscoso / Arvanitis, G. / Moustakas, K. / Hoang-Xuan, N. / Nguyen, E. R. / Tran, M. / Lejemble, T. / Barthe, L. / Mellado, N. / Romanengo, C. et al. | 2019
Online Gesture Recognition
Caputo, F. M. / Burato, S. / Pavan, G. / Voillemin, T. / Wannous, H. / Vandeborre, J. P. / Maghoumi, M. / Taranta II, E. M. / Razmjoo, A. / LaViola Jr., J. J. et al. | 2019
Monocular Image Based 3D Model Retrieval
Li, Wenhui / Liu, Anan / Nie, Weizhi / Song, Dan / Li, Yuqian / Wang, Weijie / Xiang, Shu / Zhou, Heyu / Bui, Ngoc-Minh / Cen, Yunchi et al. | 2019
Shape Correspondence with Isometric and Non-Isometric Deformations
Dyke, R. M. / Stride, C. / Lai, Y.-K. / Rosin, P. L. / Aubry, M. / Boyarski, A. / Bronstein, A. M. / Bronstein, M. M. / Cremers, D. / Fisher, M. et al. | 2019
Matching Humans with Different Connectivity
Melzi, S. / Marin, R. / Rodolà, E. / Castellani, U. / Ren, J. / Poulenard, A. / Wonka, P. / Ovsjanikov, M. | 2019