FacePerf: Benchmarks for Face Recognition Algorithms

Authors:
David S. Bolme;Michelle Strout;J. Ross Beveridge
Affiliations:
Colorado State University, Fort Collins, Colorado 80523-1873. Email: bolme@cs.colostate.edu;Colorado State University, Fort Collins, Colorado 80523-1873. Email: mstrout@cs.colostate.edu;Colorado State University, Fort Collins, Colorado 80523-1873. Email: ross@cs.colostate.edu
Venue:
IISWC '07 Proceedings of the 2007 IEEE 10th International Symposium on Workload Characterization
Year:
2007

Citing 0
Cited 2

Tradeoffs in designing accelerator architectures for visual computing

Proceedings of the 41st annual IEEE/ACM International Symposium on Microarchitecture
PIPP: promotion/insertion pseudo-partitioning of multi-core shared caches

Proceedings of the 36th annual international symposium on Computer architecture

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we present a collection of C and C++ biometric performance benchmark algorithms called FacePerf. The benchmark includes three different face recognition algorithms that are historically important to the face recognition community: Haar-based face detection, Principal Components Analysis, and Elastic Bunch Graph Matching. The algorithms are fast enough to be useful in realtime systems; however, improving performance would allow the algorithms to process more images or search larger face databases. Bottlenecks for each phase in the algorithms have been identified. A cosine approximation was able to reduce the execution time of the Elastic Bunch Graph Matching implementation by 32%.