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.
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