Image-based coin recognition using rotation-invariant region binary patterns based on gradient magnitudes

• We propose a new feature which is robust against rotation and has high accuracies for image-based coin recognition.
• The proposed feature used gradient magnitudes for improved image-based coin recognition.
• We provide results comparing the state-of-the arts for image-based coin recognition.

Most features of image-based coin recognition have been based on histogram information to achieve rotation-invariant property. However, discrimination of the features based on histogram information can be reduced by ignoring local spatial structure. In this paper, we propose a novel feature of image-based coin recognition that exploits a spatial structure. In order to consider the structure of a coin, rotation-and-flipping-robust region binary patterns (RFR) is adopted. The proposed method computes gradient magnitudes in a coin image, and extracts RFR using local difference magnitude transform to increase the accuracy of coin recognition. Comparative experiments with a number of state-of-the-art methods have been performed on the MUSCLE CIS-Benchmark Preview data set. The experimental results showed that the proposed method outperformed the state of the art methods in terms of recognition accuracy, smaller feature dimension, and shorter feature extraction time.