Fingerprint Classification Using Deep Convolutional Neural Network

Fingerprint classification is a method of reducing the number of candidates needed by fingerprint recognition systems to determine if a fingerprint picture matches one in the database. Deep learning has gained a lot of attraction in the recent decade including natural language processing, digital image processing, speech recognition, handwritten digit recognition, medical picture assessments, and so on. The subject of this paper is to explore the factors affecting fingerprint classification using a convolutional neural network and to train and test a deep CNN model, The CNN model includes two serial stages, a preprocessing phase which is used to enhance the fingerprint images qualities, and post-processing phase which used to train the classification model. This has been accomplished by designing a new deep convolutional neural network model for this work. The Convolutional neural network model achieved outstanding classification accuracy on the fingerprint. This experiment used the NIST DB4 dataset which contains 4,000 fingerprints images with five labels. Separately, each label of this database comprises almost 800 fingerprint samples with dimension of 512 x 512. To lower the training time required we reduced the fingerprint images up to 200 x 200 dimension. the study achieves 99.2% of classification accuracy with a zero-rejection rate.