Fingerprint Minutiae Detection Based on Multi-scale Convolution Neural Networks

Minutiae points are defined as the minute discontinuities of local ridge flows, which are widely used as the fine level features for fingerprint recognition. Accurate minutiae detection is important and traditional methods are often based on the hand-crafted processes such as image enhancement, binarization, thinning and tracing of the ridge flows etc. These methods require strong prior knowledge to define the patterns of minutiae points and are easily sensitive to noises. In this paper, we propose a machine learning based algorithm to detect the minutiae points with the gray fingerprint image based on Convolution Neural Networks (CNN). The proposed approach is divided into the training and testing stages. In the training stage, a number of local image patches are extracted and labeled and CNN models are trained to classify the image patches. The test fingerprint is scanned with the CNN model to locate the minutiae position in the testing stage. To improve the detection accuracy, two CNN models are trained to classify the local patch into minutiae v.s. non-minutiae and into ridge ending v.s. bifurcation, respectively. In addition, multi-scale CNNs are constructed with the image patches of varying sizes and are combined to achieve more accurate detection. Finally, the proposed algorithm is tested the fingerprints of FVC2002 DB1 database. Experimental results and comparisons have been presented to show the effectiveness of the proposed method.