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.

Mario Imandito and Suharjito, “Face Recognition on Low-Resolution Image using Multi Resolution Convolution Neural Network and Antialiasing Method” International Journal of Advanced Computer Science and Applications(IJACSA), 10(12), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0101257

@article{Imandito2019,
title = {Face Recognition on Low-Resolution Image using Multi Resolution Convolution Neural Network and Antialiasing Method},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0101257},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0101257},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {12},
author = {Mario Imandito and Suharjito}
}