Implementation of K-Nearest Neighbors face recognition on low-power processor

被引：1
|
作者：
Setiawan, Eko [1 ]
Muttaqin, Adharul [1 ]
机构：
[1] Program of Information Technology and Computer Science, Brawijaya University, Veteran Road No. 8 Malang, Jawa Timur
来源：
Telkomnika (Telecommunication Computing Electronics and Control) | 2015年 / 13卷 / 03期
关键词：
ARM processor; Face recognition; K-Nearest Neighbors;
D O I：
10.12928/telkomnika.v13i3.713
中图分类号：
学科分类号：
摘要：
Face recognition is one of early detection in security system. Automation encouragesimplementation of face recognition in small and compact devices. Most of face recognition researchfocused only on its accuracy and performed on high-speed computer. Face recognition that isimplemented on low-cost processor, such as ARM processor, needs proper algorithm. Our researchinvestigate K-Nearest Neighbor (KNN) algorithm in recognizing face on ARM processor. This researchsought best k-value to create proper face recognition with low-power processor. The proposed algorithmwas tested on three datasets that were Olivetti Research Laboratory (ORL), Yaleface and MUCT. OpenCVwas chosen as main core image processing library, due to its high-speed. Proposed algorithm wasimplemented on ARM11 700MHz. 10-fold cross-validation showed that KNN face recognition detected 91.5% face with k=1. Overall experiment showed that proposed algorithm detected face on 2.66 s on ARM processor. © 2015 Universitas Ahmad Dahlan.
引用
收藏
页码：949 / 954
页数：5
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