A kernel logit approach for face and non-face classification
A kernel logit approach for face and non-face classification
复制标题
用于人脸和非人脸分类的核 Logit 方法
DOI：
10.1109/acv.2002.1182165
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发表时间：
2002
期刊：
影响因子：
--
通讯作者：
Takio Kurita
中科院分区：
文献类型：
--
作者：
O. Hasegawa;Takio Kurita
本文介绍了一种用于人脸和非人脸分类的核logit方法。该方法基于多项logit模型（MLM）和“核特征复合向量”的结合使用。MLM是用于多类模式分类的神经网络模型之一，并且应该在分类性能上等于或优于线性分类方法。“核特征复合向量”是几何图像特征和核特征的复合特征向量。通过使用从可用的面部数据库等收集的面部图像和非面部图像（面部：训练100，交叉验证300，测试325，非面部：训练200，交叉验证1000，测试1000）进行评估和比较实验。实验结果表明，该方法优于支持向量机（SVM）和核Fisher判别分析（KFDA）。
This paper introduces a kernel logit approach for face and non-face classification. The approach is based on the combined use of the multinomial logit model (MLM) and "kernel feature compound vectors." The MLM is one of the neural network models for multiclass pattern classification, and is supposed to be equal or better in classification performance than linear classification methods. The "kernel feature compound vectors" are compound feature vectors of geometric image features and Kernel features. Evaluation and comparison experiments were conducted by using face and non,face images (Face: training 100, cross-validation 300, test 325, Non-face : training 200, cross-validation 1000, test 1000) gathered from the available face databases and others. The experimental result obtained by the proposed method was better than the results obtained by the Support Vector Machines (SVM) and the Kernel Fisher Discriminant Analysis (KFDA).