Robust and effective componentbased banknote recognition by SURF features

Abstract—We develop a novel camera-based computer vision technology to automatically recognize banknotes to assist visu-ally impaired people. Our banknote recognition system is robust and effective with the following features: 1) high accuracy: high true recognition rate and low false recognition rate; 2) robust-ness: handles a variety of currency designs and bills in various conditions; 3) high efficiency: recognizes banknotes quickly; and 4) ease of use: helps blind users to aim the target for image cap-ture. To make the system robust to a variety of conditions includ-ing occlusion, rotation, scaling, cluttered background, illumination change, viewpoint variation, and worn or wrinkled bills, we pro-pose a component-based framework by using speeded up robust features (SURF). Furthermore, we employ the spatial relationship of matched SURF features to detect if there is a bill in the camera view. This process largely alleviates false recognition and can guide the user to correctly aim at the bill to be recognized. The robust-ness and generalizability of the proposed system are evaluated on a dataset including both positive images (with U.S. banknotes) and negative images (no U.S. banknotes) collected under a variety of conditions. The proposed algorithm achieves 100 % true recogni-tion rate and 0 % false recognition rate. Our banknote recognition system is also tested by blind users. Index Terms—Banknote recognition, blind and visually impaired, component based, computer vision, speeded up robust feature (SURF). I