Evaluation of banknote identification methodologies based on local and deep features. (2nd March 2023) - Sousa, Leonardo P.; Veras, Rodrigo M.S.; Vogado, Luis H.S.; Neto, Laurindo S. Britto; Silva, Romuere R.V.; Araujo, Flávio H.D.; Medeiros, Fátima N.S.
There are many people with disabilities; it is estimated that 39 million people are blind and 246 million have limited vision, giving 285 million visually impaired people. Information and communication technologies can help disabled people achieve greater independence, quality of life, and inclusion in social activities by increasing, maintaining, or improving their functional capacities. This paper presents a significant evaluation of local and deep features for an automatic methodology for identifying banknotes. To determine the best local features, we evaluated a set of four point-of-interest detectors, two descriptors, seven ways of generating the image signature, and six classification methodologies. To define the deep features, we extract features using three pre-trained well-known CNNs. Additionally, we evaluated using a hybrid approach formed by combining local and deep features. In this situation, the features were selected according to their gain ratios and used as input to the classifier. Experiments performed on US dollar (USD), euro (EUR), and Brazilian real banknotes (BRL) obtained accuracy rates of 99.96%, 99.12%, and 96.92%, respectively.