@inproceedings{e9cd7d8efbf1444dbd994e28c82dea74,
title = "LBPH based improved face recognition at low resolution",
abstract = "Automatic individual face recognition is the most challenging query from the past decade in computer vision. However, the law enforcement agencies are inadequate to identify and recognize any person through the video monitoring cameras further efficiently; the blur conditions, illumination, resolution, and lighting are still the major problems in face recognition. Our proposed system operates better at the minimum low resolution of 35px to identify the human face in various angles, side poses and tracking the face during human motion. We have designed the dataset (LR500) for training and classification. This paper employs the Local Binary Patterns Histogram (LBPH) algorithm architecture to address the human face recognition in real time at the low level of resolution.",
keywords = "Face recognition, Feature extraction, LBPH, Low resolution",
author = "Aftab Ahmed and Jiandong Guo and Fayaz Ali and Farha Deeba and Awais Ahmed",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 International Conference on Artificial Intelligence and Big Data, ICAIBD 2018 ; Conference date: 26-05-2018 Through 28-05-2018",
year = "2018",
month = jun,
day = "25",
doi = "10.1109/ICAIBD.2018.8396183",
language = "British English",
series = "2018 International Conference on Artificial Intelligence and Big Data, ICAIBD 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "144--147",
booktitle = "2018 International Conference on Artificial Intelligence and Big Data, ICAIBD 2018",
address = "United States",
}