Using multi-instance enrollment to improve performance of 3D face recognition

• NCJ Number: 304486
• Author(s): Timothy C. Faltemier; Kevin W. Bowyer; Patrick J. Flynn
• Date Published: November 2008
• Length: 12 pages
• Grant Number(s): 2005-DD-CX-K078
• Agencies: NIJ-Sponsored
• Publication Type: Research (Applied/Empirical)

Annotation

This study explored the use of multi-instance enrollment as a means to improve the performance of 3D face recognition.

Abstract

Experiments were performed using the ND-2006 3D face data set, which contains 13,450 scans of 888 subjects. This is the largest 3D face data set currently available and contains a substantial amount of varied facial expression. Results indicate that the multi-instance enrollment approach outperforms a state-of-the-art component-based recognition approach, in which the face to be recognized is considered as an independent set of regions. (Publisher Abstract)