Face detection using large margin classifiers
Face detection using large margin classifiers
复制标题
使用大边缘分类器进行人脸检测
DOI：
10.1109/icip.2001.958581
复制
发表时间：
2001
期刊：
影响因子：
--
通讯作者：
N. Ahuja
中科院分区：
文献类型：
--
作者：
Ming;D. Roth;N. Ahuja
大间隔分类器在许多视觉学习任务中显示了其优势，并在视觉和图像处理领域引起了极大的关注。应用并比较了支持向量机和稀疏窗口网络两种大间隔分类器在静态灰度图像人脸检测中的应用，研究了这两种分类器的理论框架，并对实验结果进行了分析。在24,045幅图像的测试集上进行的实验表明，该方法具有较好的泛化能力和鲁棒性，与理论分析相符。
Large margin classifiers have demonstrated their advantages in many visual learning tasks, and have attracted much attention in vision and image processing communities. We apply and compare two large margin classifiers, support vector machines and sparse network of winnows, to detect faces in still gray scale images Furthermore, we study the theoretical frameworks of these classifiers and analyze the empirical results. Experiments on a test set of 24,045 images exhibit good generalization and robustness, and conform to theoretical analysis.