Neural Network in Biometrics : A Survey in Fingerprint Classification

Fingerprint classification has become a popular research topic due to its applicability in authentication and identification. There are several approaches in fingerprint classification system which are structural, syntactic, statistical and neural networks. The purpose of fingerprint classfication is to categorize a fingerprint into certain category based on its global pattern configuration. The analysis of comparisons between neural-network and non-neural network approaches have pointed out the advantages of using neural network in fingerprint classification. The results show that the combination of neural networks with other machine learning approach outperforms the neural networks and machine learning approach is suggested in this paper. The clear advantages of supervised and unsupervised learning in neural networks methods support the objective of this study that to suggest the neural network approach for fingerprint classification. A model of neural network combined with machine learning approach (SOM-LVQ and MLP) is proposed at the end of this study. SOM-LVQ is used for pre-classification and MLP classifier is used for classification

Fingerprint classification with combined neural networks

Biometric identification has been widely used in identifying a genuine person from an impostor. Fingerprint identification is becoming a very popular biometric identification technique because it has special properties: fingerprints are unique and unchangeable. With increased processing capability of computers and larger the size of fingerprint databases are increased, the demand for higher speed processing and greater processing capacity for automatic fingerprint identification systems (AFIS) has increased. APIS consists of fingerprint feature acquisition, fingerprint classification and fingerprint matching. Fingerprint classification plays a key role in fingerprint identification as efficient and accurate algorithms cannot only reduce the search time for searching large fingerprint databases, but they can also reduce the number of fingerprints that need to be searched.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

On the use of convolutional neural networks for robust classification of multiple fingerprint captures

Fingerprint classification is one of the most common approaches to accelerate the identification in large databases of fingerprints. Fingerprints are grouped into disjoint classes, so that an input fingerprint is compared only with those belonging to the predicted class, reducing the penetration rate of the search. The classification procedure usually starts by the extraction of features from the fingerprint image, frequently based on visual characteristics. In this work, we propose an approach to fingerprint classification using convolutional neural networks, which avoid the necessity of an explicit feature extraction process by incorporating the image processing within the training of the classifier. Furthermore, such an approach is able to predict a class even for low-quality fingerprints that are rejected by commonly used algorithms, such as FingerCode. The study gives special importance to the robustness of the classification for different impressions of the same fingerprint, aiming to minimize the penetration in the database. In our experiments, convolutional neural networks yielded better accuracy and penetration rate than state-of-the-art classifiers based on explicit feature extraction. The tested networks also improved on the runtime, as a result of the joint optimization of both feature extraction and classification

Biometric presentation attack detection: beyond the visible spectrum

The increased need for unattended authentication in multiple scenarios has motivated a wide deployment of biometric systems in the last few years. This has in turn led to the disclosure of security concerns specifically related to biometric systems. Among them, presentation attacks (PAs, i.e., attempts to log into the system with a fake biometric characteristic or presentation attack instrument) pose a severe threat to the security of the system: any person could eventually fabricate or order a gummy finger or face mask to impersonate someone else. In this context, we present a novel fingerprint presentation attack detection (PAD) scheme based on i) a new capture device able to acquire images within the short wave infrared (SWIR) spectrum, and i i) an in-depth analysis of several state-of-theart techniques based on both handcrafted and deep learning features. The approach is evaluated on a database comprising over 4700 samples, stemming from 562 different subjects and 35 different presentation attack instrument (PAI) species. The results show the soundness of the proposed approach with a detection equal error rate (D-EER) as low as 1.35% even in a realistic scenario where five different PAI species are considered only for testing purposes (i.e., unknown attacks

Neural Networks for Fingerprint Recognition

After collecting a data base of fingerprint images, we design a neural network algorithm for fingerprint recognition. When presented with a pair of fingerprint images, the algorithm outputs an estimate of the probability that the two images originate from the same finger. In one experiment, the neural network is trained using a few hundred pairs of images and its performance is subsequently tested using several thousand pairs of images originated from a subset of the database corresponding to 20 individuals. The error rate currently achieved is less than 0.5%. Additional results, extensions, and possible applications are also briefly discussed