A Framework towards Using Eigen Iris, Minutiae Thumb and DNA Sequence Features for Personal Identification

Biometric systems based on a single physiological or behavioral characteristic may not be able to identify a person correctly. This paper presents an efficient and reliable multimodal biometric identification system which is based on minutiae thumb, Eigen iris and DNA sequence features. In this method compressed form of Short tandem repeat (STR) part of DNA sequence, compressed thumbprint and compressed iris image (Eigen values) of a person are used for further identification of an individual. Therefore, personal identification including identical twins and dead person’s cases will become easier in using this method. Our technique will correctly identify a person (living or dead) on the basis of his thumbprint, iris image and DNA sequence features. We have used thumbprint and iris images for identifying a live person. But, for identifying a dead person we have used STR part of DNA sequence. We tested our method for 100 samples of thumbprints and iris images of CASIA database and we found that our multimodal method is able to correctly identify each and every individual including identical twin on the basis of thumbprints

[1]  John P. Baker,et al.  Fusing multimodal biometrics with quality estimates via a Bayesian belief network , 2008, Pattern Recognit..

[2]  Ioan Tabus,et al.  An efficient normalized maximum likelihood algorithm for DNA sequence compression , 2005, TOIS.

[3]  Michael G. Strintzis,et al.  A 3D face and hand biometric system for robust user-friendly authentication , 2007, Pattern Recognit. Lett..

[4]  D. Reynolds,et al.  Authentication gets personal with biometrics , 2004, IEEE Signal Processing Magazine.

[5]  Kimihiro Yamanaka,et al.  A Biometric Identification Using the Motion of Fingers , 2009, 2009 International Conference on Biometrics and Kansei Engineering.

[6]  Sharath Pankanti,et al.  An identity-authentication system using fingerprints , 1997, Proc. IEEE.

[7]  Johannes Peltola,et al.  Soft biometrics - combining body weight and fat measurements with fingerprint biometrics , 2006, Pattern Recognit. Lett..

[8]  Kamta Nath Mishra,et al.  An Efficient Horizontal and Vertical Method for Online DNA Sequence Compression , 2010 .

[9]  Michael Wagner,et al.  Robust face-voice based speaker identity verification using multilevel fusion , 2008, Image Vis. Comput..

[10]  San-yuan Zhang,et al.  Multimodal biometric identification system based on finger geometry, knuckle print and palm print , 2010, Pattern Recognit. Lett..

[11]  John Daugman,et al.  New Methods in Iris Recognition , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[12]  Kang Ryoung Park,et al.  Multimodal biometric method that combines veins, prints, and shape of a finger , 2011 .

[13]  Alejandro F. Frangi,et al.  This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. , 2022 .

[14]  Patrick Shen-Pei Wang,et al.  Biometric technologies and applications , 2007, Artificial Intelligence and Applications.

[15]  Teddy Ko,et al.  Fingerprint and Face Identification for Large User Population , 2003 .

[16]  Luiz Eduardo Soares de Oliveira,et al.  Combining different biometric traits with one-class classification , 2009, Signal Process..

[17]  Josef Kittler,et al.  A multimodal biometric test bed for quality-dependent, cost-sensitive and client-specific score-level fusion algorithms , 2010, Pattern Recognit..

[18]  Dexin Zhang,et al.  Personal Identification Based on , 2003 .

[19]  Eui Chul Lee,et al.  New Finger Biometric Method Using Near Infrared Imaging , 2011, Sensors.

[20]  Vishal Gupta,et al.  An Efficient Eigen Values Based Technique for Online Iris Image Compression and Identification , 2011, Int. J. Inf. Acquis..

[21]  Nicu Sebe,et al.  Multimodal Human Computer Interaction: A Survey , 2005, ICCV-HCI.

[22]  Michele Nappi,et al.  A multiexpert collaborative biometric system for people identification , 2009, J. Vis. Lang. Comput..

[23]  Luis A. Hernández Gómez,et al.  Usability evaluation of multi-modal biometric verification systems , 2006, Interact. Comput..

[24]  Gaurav Bhatnagar,et al.  Fractional dual tree complex wavelet transform and its application to biometric security during communication and transmission , 2012, Future Gener. Comput. Syst..

[25]  Xuelong Li,et al.  Multimodal biometrics using geometry preserving projections , 2008, Pattern Recognit..

[26]  Anil K. Jain,et al.  On-line fingerprint verification , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[27]  Arun Ross,et al.  Score normalization in multimodal biometric systems , 2005, Pattern Recognit..

[28]  S. S. Chowhan,et al.  Iris Biometrics Recognition Application in Security Management , 2008 .

[29]  Gian Luca Foresti,et al.  Audio-video biometric recognition for non-collaborative access granting , 2009, J. Vis. Lang. Comput..

[30]  Xiaoqing Ding,et al.  Multi-Biometrics Fusion for Identity Verification , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[31]  Slobodan Ribaric,et al.  A biometric identification system based on eigenpalm and eigenfinger features , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Fabio Roli,et al.  Designing multiple biometric systems: Measures of ensemble effectiveness , 2009, Eng. Appl. Artif. Intell..

[33]  Anil K. Jain,et al.  Integrating Faces and Fingerprints for Personal Identification , 1998, IEEE Trans. Pattern Anal. Mach. Intell..