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.
Keywords: face recognition, K-Nearest Neighbors, ARM processor
Author: Eko Setiawan, Adharul Muttaqin
Journal Code: jptkomputergg150027

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