Face Verification in the Wild using Similarity in Representations

被引：0
|
作者：
Miri, Maliheh [1 ]
机构：
[1] Higher Educ Complex Saravan, Elect Engn Dept, Fac Engn, Saravan 9951634145, Iran
来源：
2017 19TH CSI INTERNATIONAL SYMPOSIUM ON ARTIFICIAL INTELLIGENCE AND SIGNAL PROCESSING (AISP) | 2017年
关键词：
face verification; sparse representation; sparse representation-based classification; dictionary selection; RECOGNITION;
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：
In recent years, classification using sparse representation of signals has attracted much attention and has achieved satisfactory results compared to the conventional methods. In this paper, a classification method using sparse representation is proposed for face verification in Labeled Faces in the Wild (LFW) data. The LFW dataset involves high intra-class variations due to the uncontrolled imaging conditions. According to our experimental results, matched and mismatched pairs of the LFW data can be better classified using separate dictionaries for each image of the input pair.
引用
收藏
页码：140 / 144
页数：5
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