A Comparison of Face Detection Algorithms

Authors:
Ian R. Fasel;Javier R. Movellan
Affiliations:
-;-
Venue:
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Year:
2002

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Abstract

We present a systematic comparison of the techniques used in some of the most successful neurally inspired face detectors. We report three main findings: First, we present a new analysis of how the SNoW algorithm of Roth, Yang, and Ahuja (200) achieves its high performance. Second, we find that representations based on local receptive fields such as those in Rowley, Baluja, and Kanade consistently provide better performance than full connectivity approaches. Third, we find that ensemble techniques, especially those using active sampling such as AdaBoost and Bootstrap, consistently improve performance.