Gaussian weak classifiers based on co-occurring Haar-like features for face detection
| Autor: | David Delgado-Gómez, Sri-Kaushik Pavani, Alejandro F. Frangi |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
business.industry
Speech recognition Gaussian Context (language use) Pattern recognition Object detection Random subspace method symbols.namesake Haar-like features Artificial Intelligence Feature (computer vision) symbols Computer Vision and Pattern Recognition Artificial intelligence Face detection business Cascading classifiers Mathematics |
| Zdroj: | Pattern Analysis and Applications. 17:431-439 |
| ISSN: | 1433-755X 1433-7541 |
| Popis: | Recently, in the context of appearance-based face detection, it has been shown by Mita et al. that weak classifiers based on co-occurring, or multiple, Haar-like features provide better speed-accuracy trade-off than the widely used Viola and Jones's weak classifiers, which use only a single Haar-like feature. In this paper, we extend Mita et al.'s work by proposing Gaussian weak classifiers that fuse information obtained from the co-occurring features at the feature level, and are potentially more discriminative. Experimental results, on the standard MIT+CMU test images, show that the face detectors built using Gaussian weak classifiers achieve up to 38 % more accuracy in terms of false positives and 42 % decrease in testing time when compared to the detectors built using Mita et al.'s weak classifiers. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink