Distant face recognition based on sparse-stereo reconstruction

Authors:
Ham M. Rara;Shireen Y. Elhabian;Asem M. Ali;Mike Miller;Thomas L. Starr;Aly A. Farag
Affiliations:
CVIP Laboratory, University of Louisville, KY;CVIP Laboratory, University of Louisville, KY;CVIP Laboratory, University of Louisville, KY;CVIP Laboratory, University of Louisville, KY;CVIP Laboratory, University of Louisville, KY;CVIP Laboratory, University of Louisville, KY
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 4
Cited 1

Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Robust Approach to Face and Eyes Detection from Images with Cluttered Background

ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Face recognition: A literature survey

ACM Computing Surveys (CSUR)
Improving long range and high magnification face recognition: Database acquisition, evaluation, and enhancement

Computer Vision and Image Understanding

Remote identification of faces: Problems, prospects, and progress

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

We introduce a framework for face recognition at a distance based on sparse-stereo reconstruction. We develop a 3D acquisition system that consists of two CCD stereo cameras mounted on pan-tilt units with adjustable baseline. We first detect the facial region and extract its landmark points, which are used to initialize an AAM mesh fitting algorithm. The fitted mesh vertices provide point correspondences between the left and right images of a stereo pair; stereo-based reconstruction is then used to infer the 3D information of the mesh vertices. We perform experiments regarding the use of different features extracted from these vertices for face recognition. The cumulative rank curves (CMC), which are generated using the proposed framework, confirms the feasibility of the proposed work for long distance recognition of human faces with respect to the state-of-the-art [3].