Indexing Iris Biometric Database Using Energy Histogram of DCT Subbands

The key concern of indexing is to retrieve small portion of database for searching the query. In the proposed paper iris database is indexed using energy histogram. The normalised iris image is divided into subbands using multiresolution DCT transformation. Energy based his- togram is formed for each subband using all the images in the database. Each histogram is divided into fixed size bins to group the iris images having similar energy value. The bin number for each subband is obtained and all subands are traversed in Morton order to form a global key for each image. During database preparation the key is used to traverse the B tree. The images with same key are stored in the same leaf node. For a given query image, the key is generated and tree is traversed to end up to a leaf node. The templates stored at the leaf node are retrieved and compared with the query template to find the best match. The proposed indexing scheme is showing considerably low penetration rate of 0.63%, 0.06% and 0.20% for CASIA, BATH and IITK iris databases respectively.

[1]  Venu Govindaraju,et al.  Indexing Biometric Databases Using Pyramid Technique , 2005, AVBPA.

[2]  U. Jayaraman,et al.  An Indexing technique for biometric database , 2008, 2008 International Conference on Wavelet Analysis and Pattern Recognition.

[3]  Arun Ross,et al.  Indexing iris images , 2008, 2008 19th International Conference on Pattern Recognition.

[4]  Dong Wu,et al.  Image retrieval based on subband energy histograms of reordered DCT coefficients , 2002, 6th International Conference on Signal Processing, 2002..

[5]  Phalguni Gupta,et al.  An efficient indexing scheme for binary feature based biometric database , 2007, SPIE Defense + Commercial Sensing.

[6]  Shaogang Gong,et al.  Audio- and Video-based Biometric Person Authentication , 1997, Lecture Notes in Computer Science.

[7]  S. A. Khayam.  The Discrete Cosine Transform ( DCT ) : Theory and Application 1 , 2003 .

[8]  James L. Wayman,et al.  Error rate equations for the general biometric system , 1999, IEEE Robotics Autom. Mag..

[9]  WaveletsElif Albuz,et al.  Scalable Image Indexing and Retrieval using , 1998 .

[10]  D. Kerbyson,et al.  Circle detection using Hough transform filters , 1995 .

[11]  Ashfaq A. Khokhar,et al.  Scalable Color Image Indexing and Retrieval Using Vector Wavelets , 2001, IEEE Trans. Knowl. Data Eng..

[12]  Phalguni Gupta,et al.  Indexing Multimodal Biometric Databases Using Kd-Tree with Feature Level Fusion , 2008, ICISS.

[13]  John Daugman,et al.  How iris recognition works , 2002, IEEE Transactions on Circuits and Systems for Video Technology.

[14]  Anil K. Jain,et al.  Handbook of Fingerprint Recognition , 2005, Springer Professional Computing.

[15]  Dexin Zhang,et al.  Efficient iris recognition by characterizing key local variations , 2004, IEEE Transactions on Image Processing.

[16]  Josef Kittler,et al.  Audio- and Video-Based Biometric Person Authentication, 5th International Conference, AVBPA 2005, Hilton Rye Town, NY, USA, July 20-22, 2005, Proceedings , 2005, AVBPA.