Fingerprint recognition using convolution neural network with inversion and augmented techniques

Fingerprints are considered as one of the most important and prominent feature for an individual identification. Due to their consistency and reliability in biometric feature identification, they are most widely used for biometric recognition systems. In these systems, the relevant feature extraction plays important role in achieving required classification accuracy. In recent time, deep learning techniques are being used for fingerprint recognition with more accuracy and efficient results. Major difficulty which has been reported in previous researches, is the limited size of samples. Therefore, we propose two approaches, inversion and multi augmentation to augment the sample size with newly generated images for each feature map. Besides this, multiple networks are used simultaneously for feature extraction from newly generated images in parallel mode. Deep neural network architectures are used with proposed inversion methods and multi augmentation methods to classify the samples of fingerprints for personnel identification and verification. Pre-trained deep convolutional models like VGG16, VGG19, ResNet50 and InceptionV3 are fine-tuned with new processed fingerprint images for feature extraction and classification. The collective samples of fingerprints have been classified into 10 classes. The simulation results have been obtained with different optimizers and it has been observed that VGG 19 model exhibits the accuracies of 88 % and 93 % with inversion and multi augmentation approaches respectively. Whereas, VGG16 model exhibits 93 % with inversion approach and 97 % with multi augmentation approach. Thus, the proposed approach exhibits the accuracy up to 97 % with VGG16 model which is significantly much higher than that of any other model with the same dataset FVC2000_DB4.