Fingerprint matching using multi-dimensional ANN

Fingerprint matching is one of the most widely used biometric technique for personal identification. This identification is achieved in this work by using the concept that every fingerprint has a unique pattern of distribution of the minutiae points. In this paper, a new method of recognition of this pattern of distribution of the minutiae points of an enhanced image is considered by using a multi-dimensional artificial neural network (MDANN). The proposed technique has the distinct advantage of using the entire resized minutiae image as an input at once. It is capable of excellent pattern recognition properties as the distribution of the minutiae points are used directly for recognition. The proposed approach shows significant promise and potential for improvement, compared with the other conventional matching techniques with regard to time and efficiency of results.