Face Recognition on Low-Resolution Image using Multi Resolution Convolution Neural Network and Antialiasing Method

Mario Imandito,Suharjito

doi:10.14569/ijacsa.2019.0101257

Abstract

Video surveillance applications usually take pictures of faces that have a low resolution (12x12) due to distance, lighting and shooting angles Most of face recognition algorithm have the poor performance accuracy and poor identify face on low resolution. Based on the problem, identifying the face of the query in low resolution, based on high resolution (64x64) proves to be a huge challenge. The aim of this research is to develop a new model for face recognition of low-resolution image in order to increase the accuracy of recognition. A Multi-Resolution Convolutional Neural Network (MRCNN) is proposed to address the problem. First, Antialiasing is used in preprocessing phase, then use MRCNN to extract the feature of the image. LWF (Labeled Face in Wild) will be used to evaluate the model. The result of this study is increasing the accuracy of face recognition on low-resolution image compared to the previous MRCNN model.

Highlights

Security in one of the most concern problem in almost every application
The existing face recognition system such as Principal Component Analysis (PCA) [1], Linear Discriminant Analysis (LDA) [2], and the most popular Super Resolution (SR) [3] have achieved satisfactory performance, in case that the face images that collected are in high resolution and are well aligned
The resized high resolution (HR) image from input and generated HR is combined as an multi-resolution convolutional neural network (MRCNN) modeling input in the training and testing process

Summary

Introduction

Security in one of the most concern problem in almost every application. Face recognition is one of many samples of security method. The existing face recognition system such as Principal Component Analysis (PCA) [1], Linear Discriminant Analysis (LDA) [2], and the most popular Super Resolution (SR) [3] have achieved satisfactory performance, in case that the face images that collected are in high resolution and are well aligned. In case of video surveillance system, as the face target are far away from the cameras, the captured facial images are usually in low resolution (32x32). This affects the accuracy of facial recognition system. Video surveillance system usually used to identify someone in a secure area such as workspace, data center etc. To identify person in low resolution based on high resolution image proves to be a huge challenge

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Conclusion