Incremental Face Recognition By Tagged Neural Cliques

被引：0

作者：

Gooya, Ehsan Sedgh ^{[1
]}

Pastor, Dominique ^{[2
]}

机构：

[1] Inst Mines Telecom, Telecom Bretagne, UMR CNRS Lab STICC 6285, Dept Elect, Technopole Brest Iroise CS 83818, F-29238 Brest, France

[2] Inst Mines Telecom, Telecom Bretagne, UMR CNRS Lab STICC 6285, Dept Signal & Commun, Technopole Brest Iroise CS 83818, F-29238 Brest, France

来源：

COGNITIVE 2017: THE NINTH INTERNATIONAL CONFERENCE ON ADVANCED COGNITIVE TECHNOLOGIES AND APPLICATIONS | 2017年

关键词：

Face recognition; incremental learning; neural tagged cliques; SIFT (Scale-Invariant Feature Transform) features;

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：

We present a system aimed at performing an incremental learning based on a neural network of tagged cliques for face recognition. A crucial component of the system is the network of neural tagged cliques. In its original version, cliques are a set of binary connections linking a set of fired neurons. Tagged cliques make it then possible to identify these cliques. The incremental learning is achieved through two phases: the first one is supervised by an oracle and the second one is automatic. Experimental results on the ORL (Olivetti Research Laboratory) face database pinpoint that incremental learning significantly reduces the number of features to store and yields substantial recognition rate improvement, in comparison with no incremental learning.

引用

页码：54 / 58

页数：5

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