Proceedings Article | 17 May 2011
Proc. SPIE. 8029, Sensing Technologies for Global Health, Military Medicine, Disaster Response, and Environmental Monitoring; and Biometric Technology for Human Identification VIII
KEYWORDS: Databases, Facial recognition systems, Detection and tracking algorithms, 3D scanning, Algorithm development, 3D image processing, Error analysis, Electrical engineering, Medical research, Clouds
During the last decade research in face recognition has shifted from 2D to 3D face representations. The need
for 3D face data has resulted in the advent of 3D databases. In this paper, we first give an overview of publicly
available 3D face databases containing expression variations, since these variations are an important challenge
in today's research. The existence of many databases demands a quantitative comparison of these databases in
order to compare more objectively the performances of the various methods available in literature. The ICP
algorithm is used as baseline algorithm for this quantitative comparison for the identification and verification
scenario, allowing to order the databases according to their inherent difficulty. Performance analysis using the
rank 1 recognition rate for identification and the equal error rate for verification reveals that the FRGC v2
database can be considered as the most challenging. Therefore, we recommend to use this database further as
reference database to evaluate (expression-invariant) 3D face recognition algorithms. As second contribution,
the main factors that influence the performance of the baseline technique are determined and attempted to be
quantified. It appears that (1) pose variations away from frontality degrade performance, (2) expression types
affect results, (3) more intense expressions degrade recognition, (4) an increasing number of expressions decreases
performance and (5) the number of gallery subjects degrades performace. A new 3D face recognition algorithm
should be evaluated for all these factors.
