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

Penentuan Klas Sidik Jari Berdasarkan Arah Kemiringan Ridge

Researches on  fingerprint  classification are generally based on its features such as core and delta. Extraction of these features are generally preceded by a variety of preprocessing. In this study the classification is done directly on the fingerprint image without preprocessing. Feature used as the basis for classification is the direction of the ridge. The direction of the ridge  is determined by the slope of the blocks that are exist on every ridge. Fingerprint image is divided into blocks of size 3x3 pixels and the direction of each block is determined. Direction of the slope of the block are grouped into 8, these are  north, north-east, east, south-east, south, south-west, west and north-west. The number of blocks in each direction form the basis of classification using Learning Vector Quantization network (LVQ). This study used 80 data samples from the database of FVC2004. This model obtained classification accuracy of up to 86.3%. Keywords—fingerprint, classification, ridge, LV

Componente de indexación de huellas dactilares basado en características globales

The fingerprint orientation field is a widely used feature for developing indexing strategies. Such feature brings stability and decreases the response times during the identification process. The use of attribute relational graphs and dynamic masks can exploit the features provided by the partitioning scheme. The penetration index and the error rate for each of the proposed strategies were determined. To verify the results obtained were used the databases provided by the Fingerprint Verification Competition. Both search schemes reveal the facilities offered by the orientation field for guiding the searching process; reducing the number of comparisons in 53.24%; proving its stability against different fingerprint image qualities. Based on the results, it was proven that the adoption of such indexing strategies will reduce the response time in the identification module proposed by the Identification and Digital Security Center at the University of Informatics Sciences.El campo de orientación de la huella dactilar como característica global es fundamental para el desarrollo de una estrategia de indexación, aportando estabilidad y disminuyendo los tiempos de respuesta durante el proceso de búsqueda de un sistema de identificación basado en huellas dactilares. El empleo de grafos relacionales de atributos y máscaras dinámicas permite explotar las características brindadas por el esquema de particionado para la búsqueda. De cada una de las estrategias propuestas se determinó el índice de penetración de la base de datos, así como el error en que incurren los algoritmos de indexación implementados. Para corroborar los resultados obtenidos fueron empleados los bancos de datos aportados por la Competencia de verificación de huellas dactilares. La implementación computacional de las estrategias de búsquedas propuestas demuestra las virtudes que ofrece el campo de orientación para dirigir el proceso de búsqueda, logrando reducir el número de comparaciones en un 53.24% como promedio, comportándose de manera estable ante imágenes de huellas dactilares de diferente calidad. Basado en los resultados del estudio se concluyó que la adopción de dichas estrategias de indexación permitirá reducir el tiempo de respuesta en el módulo de identificación de individuos basado en patrones de la huella dactilar propuesto por el Centro de Identificación y Seguridad Digital de la Universidad de las Ciencias Informáticas

KLASIFIKASI DISTORSI AKUISISI CITRA SIDIK JARI BERBASIS MULTI FITUR MENGGUNAKAN METODE SUPPORT VECTOR MACHINE

Kualitas data citra sidik jari merupakan faktor utama penentu tingkat akurasi keberhasilan proses pengenalan sidik jari dalam sistem biometrik. Kualitas citra sidik jari yang mengandung beberapa parameter penting sebagai prasayarat pemrosesan data lebih lanjut, terdefenisi dengan jelas pada saat proses akuisisi. Permasalahannya adalah pada tahap ini, ditemui fakta bahwa sangat dibutuhkan informasi jenis distorsi citra sidik jari agar dapat ditentukan metode perbaikan citra yang tepat sehingga dihasilkan tingkat akurasi pengenalan yang tinggi. Penelitian ini dilakukan untuk menghadirkan cara alternatif proses klasifikasi jenis distorsi akuisisi citra sidik jari ke dalam tiga kategori (kering, netral dan berminyak) menggunakan metode Support Vector Machine (SVM) berbasis multi fitur citra sidik jari. yang terdiri dari: nilai intensitas rata-rata (RR), nilai varians (VAR), standar deviasi (STD), nilai koherensi (KOH), skor kejelasan ridge-valley (CS) dan rasio ketebalan ridge-valley (TR). Penelitian ini dititikberatkan pada penentuan nilai strandar parameter jenis distorsi akuisisi citra sidik jari dan analisis metode klasifikasi yang difokuskan pada pengaruh perbedaan penggunaan fungsi kernel SVM terhadap rasio kebenaran klasifikasi sidik jari. Hasil penelitian menunjukan bahwa fungsi kernel SVM yang paling optimal untuk klasifikasi jenis distorsi akuisisi citra sidik jari berbasis multi fitur ke dalam tiga kategori kering, netral dan berminyak adalah kernel polynomial dengan nilai c=108

An Effective Method for Fingerprint Classification

[[abstract]]In this paper, we present a fast and precise method for fingerprint classification. The proposed method directly extracts the directional information from the thinned image of the fingerprint.We use an octagon mask to search the center point of the region of interest and consider both the direction information and the singular points in the region of interest to classify the fingerprints. In the system, not only is the amount of computation reduced but also can the extracted information be used for identification on AFIS. The system has been tested all 4000 fingerprint images on the NIST special fingerprint database 4. The classification accuracy reaches 93.425% with no rejection for 4-class classification problem.[[notice]]補正完畢[[incitationindex]]E