MaxHash for Fast Face Recognition and Retrieval

被引：2
作者：
Al Kobaisi, Ali [1 ,2 ]
Wocjan, Pawel [3 ]
机构：
[1] Univ Cent Florida, Dept Elect & Comp Engn, Orlando, FL 32816 USA
[2] Univ Wasit, Wasit, Iraq
[3] Univ Cent Florida, Dept Comp Sci, Orlando, FL 32816 USA
来源：
2019 6TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE (CSCI 2019) | 2019年
关键词：
FaceNet; Face Recogthtion; Person Identification; Learmng to Hash; Convolutional Neural Networks;
D O I：
10.1109/CSCI49370.2019.00122
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents a fast method for recogthtion and retrieval of face images in large face datasets. In this approach, we generate binary hash codes for deep face features extracted using FaceNet model. Ulilike the distance of real valued feature vectors, the distance in the Hamming space is computed with a simple XOR operation. We generate a candidate set usiiig the Hamming distance metric, then a fine grained search is used to find the corresponding face picture in this small set. Experiments on LFW dataset show that for 64 bit hash codes an image of the corresponding person is always in the candidates set of only 48 items.
引用
收藏
页码：652 / 656
页数：5
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