Implementation of K-Nearest Neighbors Face Recognition on Low-power Processor
Abstract: Face recognition is
one of early detection in security system. Automation encourages implementation
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 algorithm was tested on three datasets that were Olivetti Research
Laboratory (ORL), Yaleface and MUCT. OpenCV was 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.
Author: Eko Setiawan, Adharul
Muttaqin
Journal Code: jptkomputergg150027
