Innovative methods for low-resolution image recognition in face and license plate tasks

Jiangqi Li; Yuhan Chen; Xuelin Wang; Benying Tan

doi:10.1117/12.3012190

21 December 2023 Innovative methods for low-resolution image recognition in face and license plate tasks

Jiangqi Li, Yuhan Chen, Xuelin Wang, Benying Tan

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 1297032 (2023) https://doi.org/10.1117/12.3012190
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

Despite the great performance achieved by deep learning-based image recognition techniques in recent years, low-resolution image recognition is still challenging in terms of improving model performance and reducing cost consumption for practical applications. In this task, we propose a solution to achieve low-resolution image recognition by appropriately reducing the resolution of the images in the database so that there is a better match between them and the image to be recognized. In the task of face recognition, we use the Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM) to determine whether the image contains a face or not, followed by the KNN model for face information recognition. In the number plate recognition task, we use the Convolutional Neural Network (CNN) model to recognize number plates. The experiments were validated on two publicly available datasets. The experimental results show that it can perform better in accomplishing low-resolution image recognition tasks while saving computational resources and other costs.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiangqi Li, Yuhan Chen, Xuelin Wang, and Benying Tan "Innovative methods for low-resolution image recognition in face and license plate tasks", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 1297032 (21 December 2023); https://doi.org/10.1117/12.3012190

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