Fingerprint Classification

Automatická identifikace založená na otiscích prstů vyžaduje porovnání otisku prstu s velkým množstvím otisků prstů uložených v databázi. Klasifikace otisků prstů poskytuje důležitý mechanizmus indexování v databázích otisků prstů, který snižuje čas vyhlehledání a výpočetní nároky. Tato práce se zabývá Galton-Henryho klasifikačním systémem. Klasifikační metoda je založena na poli orientací a detekci singularit pomocí Poincaréova indexu.Automatic identification based on fingerprints requires the input fingerprint to be matched with a large number of fingerprints stored in a database. Fingerprint classification provides an important indexing mechanism in fingerprint databases that reduces the search time and computational complexity. This thesis is dealing with Galton-Henry's  classification system. The classification method is based on orientation field and detection singularities using Poinaceré index.

FINGERPRINTS PREPROCESSING USING WALSH FUNCTIONS

Minutiae classification and fingerprint classification in fingerprint evaluating process are very important. Fingerprint image contains about 150 minutiae’s. When we compare two fingerprint images, we compare latent and non latent fingerprint and we try to find 12 minutiae’s placed on the same position on latent and non latent fingerprint images. After fingerprint image pre-processing we can perform classification or we can try to find minutiae. In this paper we describe the process of minutiae classification for comparison purposes. For that purpose we use Walsh function and Walsh transforms. Paper describes minutiae classification which is relatively new in recognition systems and gives contribution for development of practical fingerprint recognition systems. Paper also gives contribution in the theoretical part due to the fact that Walsh functions were not implemented in fingerprint pre-processing systems so far. The new symbolic database model for fingerprint storage gives multifunctional foundations for future research

An Efficient Fingerprint Identification using Neural Network and BAT Algorithm

The uniqueness, firmness, public recognition, and its minimum risk of intrusion made fingerprint is an expansively used personal authentication metrics. Fingerprint technology is a biometric technique used to distinguish persons based on their physical traits. Fingerprint based authentication schemes are becoming increasingly common and usage of these in fingerprint security schemes, made an objective to the attackers. The repute of the fingerprint image controls the sturdiness of a fingerprint authentication system. We intend for an effective method for fingerprint classification with the help of soft computing methods. The proposed classification scheme is classified into three phases. The first phase is preprocessing in which the fingerprint images are enhanced by employing median filters. After noise removal histogram equalization is achieved for augmenting the images. The second stage is the feature Extraction phase in which numerous image features such as Area, SURF, holo entropy, and SIFT features are extracted. The final phase is classification using hybrid Neural for classification of fingerprint as fake or original. The neural network is unified with BAT algorithm for optimizing the weight factor