An Intelligent System for Automatic Fingerprint Identification using Feature Fusion by Gabor Filter and Deep Learning

This paper introduces an intelligent computational approach to automatically authenticate fingerprint for personal identification and verification. The feature vector is formed using combined features obtained from Gabor filtering technique and deep learning technique such as Convolutional Neural Network (CNN). Principle Component Analysis (PCA) has been performed on the feature vectors to reduce the overfitting problems in order to make the classification results more accurate and reliable. A multiclass classifier has been trained using the extracted features. Experiments performed using standard public databases demonstrated that the proposed approach showed better performance with regard to accuracy (99.87%) compared to the more recent classification techniques such as Support Vector Machine (97.86%) or Random Forest (95.47%). However, the proposed method also showed higher accuracy compared to other validation approaches such as K-fold (98.89%) and generalization (97.75%). Furthermore, these results were supported by confusion matrix results where only 10 failures were found when tested with 5000 images

Core Point Pixel-Level Localization by Fingerprint Features in Spatial Domain

Singular point detection is a primary step in fingerprint recognition, especially for fingerprint alignment and classification. But in present there are still some problems and challenges such as more false-positive singular points or inaccurate reference point localization. This paper proposes an accurate core point localization method based on spatial domain features of fingerprint images from a completely different viewpoint to improve the fingerprint core point displacement problem of singular point detection. The method first defines new fingerprint features, called furcation and confluence, to represent specific ridge/valley distribution in a core point area, and uses them to extract the innermost Curve of ridges. The summit of this Curve is regarded as the localization result. Furthermore, an approach for removing false Furcation and Confluence based on their correlations is developed to enhance the method robustness. Experimental results show that the proposed method achieves satisfactory core localization accuracy in a large number of samples

Jedna klasa biometrijskog kriptosistema zasnovanog na konvolucionim neuronskim mrežama

U ovoj doktorskoj disertaciji predložen je novi biometrijski kriptosistem otisaka prstiju baziran na sistemu fazi povezivanja i dubokih konvolucionih neuronskih mreža. Centralni doprinos rada predstavlja novi pristup automatskom izdvajanju obeležja fiksne dužine iz otisaka prstiju, u potpunosti zasnovanom na konvolucionim neuronskim mrežama. Predloženom kvantizacijom obeležja kodovanjem sa dva bita, biometrijski šabloni su prevedeni u binarni domen, što je omogućilo primenu XOR biometrije i razvoj biometrijskog kriptosistema koji se može koristiti za upravljanje ključevima (engl. key-release) ili za zaštitu šablona. Problem varijabilnosti biometrijskih podataka marginalizovan je primenom BCH koda za korekciju grešaka, koji radi na nivou bloka što ga čini otpornim na poznate statističke napade. Predloženi biometrijski kriptosistem sistem može upravljati dužinom ključeva od 265 bita, što zadovoljava potrebe savremenih kriptografskih sistema, uz prihvatljivu marginu EER greške od 1%. Evaluacija eksperimentalnih rezultata potvrđuje značajan napredak u odnosu na druge biometrijske kriptosisteme i sisteme za poređenje otisaka na osnovu njihove teksture