A new boostrapping strategy for the adaboost-based face detector
Date
2005
Authors
Chin, Tat-Jun
Suter, David
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Report
Citation
Statement of Responsibility
Tat-Jun Chin and D. Suter
Conference Name
Abstract
False alarms occur in the face detection process when non-face sub-images are classified as face images, and are a major problem in face detection. Usually practitioners perform “bootstrapping”to refine the face detector to account for the false alarms (or false positives). For AdaBoost-based face detection (specifically, the method propounded by Viola and Jones in [5]), two approaches of bootstrapping are used– incremental retraining of the entire face detector with the false positives as examples, or construct additional classifier stages to eliminate the false positives. While incremental retraining is costly in terms of training time and effort, additional stages will ultimately slow down the face detector, thus defeating the purpose of constructing the detector in a cascade manner for speed-ups. This technical report presents a novel bootstrapping strategy that although is similar in nature to the second approach, has some modifications with the aim of improving the efficiency of the bootstrapping procedure.
School/Discipline
School of Computer Science