The paper proposes an efficient indexing scheme for binary feature template using B+ tree. In this scheme the
input image is decomposed into approximation, vertical, horizontal and diagonal coefficients using the discrete
wavelet transform. The binarized approximation coefficient at second level is divided into four quadrants of equal
size and Hamming distance (HD) for each quadrant with respect to sample template of all ones is measured. This
HD value of each quadrant is used to generate upper and lower range values which are inserted into B+ tree.
The nodes of tree at first level contain the lower and upper range values generated from HD of first quadrant.
Similarly, lower and upper range values for the three quadrants are stored in the second, third and fourth level
respectively. Finally leaf node contains the set of identifiers. At the time of identification, the test image is
used to generate HD for four quadrants. Then the B+ tree is traversed based on the value of HD at every node
and terminates to leaf nodes with set of identifiers. The feature vector for each identifier is retrieved from the
particular bin of secondary memory and matched with test feature template to get top matches. The proposed
scheme is implemented on ear biometric database collected at IIT Kanpur. The system is giving an overall
accuracy of 95.8% at penetration rate of 34%.

An efficient indexing scheme for binary feature based biometric database

Automated fingerprint identification system (AFIS) is very popular now days for biometric security because of the uniqueness of individual's fingerprint. The need for fingerprint classification arises due to very large fingerprint databases resulting in long response time which is unsuitable for real time applications. Hence in order to reduce number of comparisons fingerprint classification is necessary. It also plays a key role in identifying fingerprints. In this paper we have proposed a new classification technique based on the detection of singular points (core and delta points) consisting of four stages. In the first stage, preprocessing of input fingerprint image is done followed by fine orientation field estimation in second stage. In the third stage, singular points are located using modified Poincare index technique and hence in the fourth stage, classification is done on the basis of these singular points. The proposed technique was tested on NIST 4 database and the results show a significant improvement in classification of different types of fingerprints.

An automated system for fingerprint classification using singular points for biometric security

AFIS (automated fingerprint identification system) is very popular now days for biometric security. Fingerprint classification plays a key role in identifying fingerprints and also helps in fingerprint matching. This paper presents a new classification technique based on the detection of singular points (core and delta points). This fingerprint classification technique consists of four steps. In the first step, preprocessing (segmentation and normalization) of input fingerprint image is done. In the second step, fine orientation field of image is estimated. In the third step, singular points are located using modified Poincare index technique. In the fourth step, classification is done on the basis of singular points. The proposed technique was tested on FVC2004 database and the results show a significant improvement in reducing the misclassification errors.

Computerized system for fingerprint classification using singular points

Sidik jari memiliki dua fitur penting yang disebut fitur lokal (minutiae) dan fitur global (singularities). Fitur lokal terdiri dari bagian-bagian detail dari sidik jari, sedangkan fitur global terdiri alur (ridge), titik pusat (core), dan titik persimpangan (delta). Fitur lokal biasanya digunakan sebagai dasar pencocokan sidik jari, sedangkan fitur global biasanya dipakai untuk dasar klasifikasi. Klasifikasi berdasarkan keberadaan dan posisi core dan delta memerlukan proses komputasi yang rumit dan panjang. Pada penelitian ini sudut kemiringan alur diteliti, dengan harapan dapat dipakai sebagai dasar klasifikasi yang lebih sederhana dan lebih cepat proses komputasinya. Pertama, citra grayscale sidik jari dikonversi menjadi citra biner, kemudian dilakukan proses thinning dan selanjutnya dikonversi menjadi citra bipolar. Kedua, dibuat 12 definisi sudut potongan alur yang melewati blok berukuran 3x3 piksel. Nilai dari piksel-piksel pada ke 12 blok tersebut digunakan sebagai target pelatihan bagi Hamming Network. Dengan memproses setiap blok berukuran 3x3 piksel dari citra sidik jari dengan Hammning Network maka sudut kemiringan potongan alur pada blok tertentu dapat ditentukan. Penelitian ini menghasilkan klasifikasi dengan ketepatan 63% dari 80 sampel pola sidik jari.

/pdf/deteksi-kemiringan-alur-pola-sidik-jari-dengan-hamming-net-1gce9m54im.pdf

Deteksi kemiringan alur pola sidik jari dengan hamming net sebagai dasar klasifikasi

hoo.com Abstract- AFIS (automated fingerprint identification system) is very popular now days for biometric security. Fingerprint classification plays a key role in identifying fingerprints and also helps in fingerprint matching. This paper presents a new classification technique based on the detection of singular points (core and delta points). This fingerprint classification technique consists of four steps. In the first step, preprocessing (segmentation and normalization) of input fingerprint image is done. In the second step, fine orientation field of image is estimated. In the third step, singular points are located using modified Poincare index technique. In the fourth step, classification is done on the basis of singular points. The proposed technique was tested on FVC2004 database and the results show a significant improvement in reducing the misclassification errors.

Classification using Singular Points

Singular points are one of the characteristic features of fingerprint images. In this paper, a new way of finding these singular points from the directional image is proposed. The image used in this system is a 256 X 256 rectangular image from which the hexagonal image will be extracted. After producing the directional image, two other images will be constructed for the core & delta candidate points. These pair of points will be selected using a method based on Hough transform and local maximum algorithm. Based on the position and number of these points a one-byte code will be assigned to that class. By performing the Fourier image of the original picture, an eight-byte code will be assigned to the image using a 4 X 4 wedge-ring detector applied to the transformed image. These codes will be used to show the dissimilarity among similar images.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Ali Jannatpour

Papers

Fingerprint classification using singular points and Fourier image