Title

Multi-view face recognition from single RGBD models of the faces

SMC Author

Noha M. Elfiky

Status

Faculty

School

School of Economics and Business Administration

Department

Business Analytics

Document Type

Article

Publication Date

7-2017

Publication Title

Computer Vision and Image Understanding

Description/Abstract

This work takes important steps towards solving the following problem of current interest: Assuming that each individual in a population can be modeled by a single frontal RGBD face image, is it possible to carry out face recognition for such a population using multiple 2D images captured from arbitrary viewpoints? Although the general problem as stated above is extremely challenging, it encompasses subproblems that can be addressed today. The subproblems addressed in this work relate to: (1) Generating a large set of viewpoint dependent face images from a single RGBD frontal image for each individual; (2) using hierarchical approaches based on view-partitioned subspaces to represent the training data; and (3) based on these hierarchical approaches, using a weighted voting algorithm to integrate the evidence collected from multiple images of the same face as recorded from different viewpoints. We evaluate our methods on three datasets: a dataset of 10 people that we created and two publicly available datasets which include a total of 48 people. In addition to providing important insights into the nature of this problem, our results show that we are able to successfully recognize faces with accuracies of 95% or higher, outperforming existing state-of-the-art face recognition approaches based on deep convolutional neural networks.

Keywords

Face recognition, Depth cameras, Manifold representations, Multi-view face recognition, RGBD models, Deep convolutional neural networks, Deep learning

Scholarly

yes

Peer Reviewed

1

DOI

10.1016/j.cviu.2017.04.008

Volume

160

First Page

114

Last Page

132

Disciplines

Business | Economics

Original Citation

Kim, D., Comandur, B., Medeiros,H., Elfiky, N.M., & Kak, A.C. (2017). Multi-View Face Recognition from Single RGBD Models of the Faces. Computer Vision and Image Understanding, 160, 114-132.

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