Tuning Asymboost Cascades Improves Face Detection
Ingrid Visentini, Christian Micheloni, Gian Luca Foresti · 2007
The face detection problem is certainly one of the most studied topics in artificial vision. This interest raises from the conscience that this is a crucial step for every system that uses biometric information. Video surveillance and security systems, biometrics, HCI and multimedia applications are some examples of systems that exploit face localization to improve their robustness. AdaBoost and AsymBoost based classifiers are widely used to achieve high performances saving computational time. In this paper, a new reactive strategy to build a strong classifier cascade is provided; at each stage of the cascade a different tradeoff between accuracy and computational complexity is explored. The results will show that this method is effective, and propose a way to construct a rapid and robust multipose detector.