A constrained generative model applied to face detection

被引：0

作者：

Feraud, R ^{[1
]}

Bernier, O ^{[1
]}

Collobert, D ^{[1
]}

机构：

[1] FRANCE TELECOM,CNET,TECHNOPOLE ANTICIPA,TNT,RIO,LAB,F-22307 LANNION,FRANCE

来源：

NEURAL PROCESSING LETTERS | 1997年 / 5卷 / 02期

关键词：

autoencoder; face detection; information theory; neural network;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A generative neural network model, constrained by non-face examples chosen by an iterative algorithm, is applied to fact: detection. To improve the generalization ability of the model, another constraint based on the minimum description length is added. This model is tested and compared with another state-of-the-art face detection system on a large image test set collected at CMU.

引用

页码：73 / 81

页数：9

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