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  1. 1. Automated Multi-modal Biometrics Identification System(AMBIS) National Crime Records Bureau, New Delhi is the central repository of totalFingerprint Biometrics being used for tracking criminals across the country. Atpresent the AFIS exists at NCRB HQ and 22 other states Headquarters. And 11more States have yet to install the system. These AFIS have been runningstandalone with least features to deliver the desired result for tracking criminals instate. The shortcomings of present AFIS have been studied and found as follows:  Most of AFIS are of outdated technology and have proprietary encoding & matching algorithms, which lack commonality & interoperability.  None of these AFIS has interstate / inter AFIS connectivity module & functionality and therefore no Data Portability & Interoperability is achieved even amongst various versions of same vendor and AFIS of other vendors.  All AFIS have miserably poor capability to search Latent Print.  No AFIS has capability to store & search palm print and is not complete package of all required core functionalities. Because of aforementioned reasons these AFIS are virtually failed to trackcriminals and have lost its credibility and usefulness. Despite the huge databaseavailability in country, a fraction of it has been digitalized and much lesser hasreached to NCRB for tracking criminals. Keeping the above facts and anomalies in the mind I have been made theChairman for National Benchmarking Committee so, that NCRB comes with stateof art system similar to one which the FBI has. It also removes all anomalies ofpresent system so that tracking of criminal becomes seamless as imminentlyrequired for the success of CCTNS. This article will deal with the strategy andexcerpt of my AMBIS (Automated Multi-Modal Biometric Identification System)report. Automated fingerprint identification systems (AFIS) have been widely usedin forensics for the past two decades, and recently they have become relevant forcivil applications as well. Whereas large-scale biometric applications require highidentification speed and reliability, multi-biometric systems that incorporatefingerprint, Iris and Face. AMBIS is acronym of Automated Multi-Modal Biometrics IdentificationSystem. It incorporates State of the Art biometric technologies to serve law 1
  2. 2. enforcement applications beyond traditional AFIS capabilities. Modalities usedtoday are as follows-  Finger (Ten print flats and rolls, latent)  Face (Mug shot and latent face) Multi modal Biometrics technology for CCTNS  Iris (Dual iris scans)  Palm (Print and latent) recognition offer a number of advantages for improving identification quality and usability.Proposed Solution for NCRB (NAFIS) Automated Fingerprint Identification System (AFIS) is a system in whichimages of known fingerprints are encoded and stored in a computer database.Utilizing this database of known fingerprints, other images of ten digit fingerprintsand unidentified latent fingerprints are then searched through the system todetermine identity. The system encodes the fingerprints that are being searched andfinds fingerprints in the system that most closely resembles the fingerprint beingsearched. A qualified examiner compares the fingerprints reported by the AFIS anddetermines if identity of the searched fingerprint (inked or latent) can beestablished. It is observed that most of the latent prints found on Scene of Crimeare Partial palm prints therefore an AFIS having Palm Print Search and Storagefacilities are also required. NAFIS is acronym of National Automated Fingerprint IdentificationSystem. The main objective of NAFIS is to provide the national level fingerprintdatabase of criminals & improve crime detection rate with the help of fingerprintidentification. It is proposed to have a National AFIS system at NCRB, which willstore Finger print data of all states. All states should have their state AFIS. Statecan deploy Remote Stations at district, sub divisional or Police Station level asrequired. All these AFIS system will be interconnected having automatic remoteupdating and query facility. AFIS having web enabled updating and queryprocessing facility will be appreciated. The NAFIS will maintain the fingerprint data in standard ANSI/NISTformat. All State’s AFIS will be connected to NAFIS with strong networkingfacility. NAFIS will follow the Server-Client architecture and also support web-based scenario. The National Automated Fingerprint Identification System (NAFIS) willprovide automated fingerprint search capabilities, latent searching capability, 2
  3. 3. electronic image storage, and electronic exchange of fingerprints and responses, 24hours a day, 365 days a year. As a result of submitting fingerprints for search would receive electronicresponses to criminal ten-print fingerprint submissions within hours.Proposed Database specification for AMBIS - During All India Directors Conference of Finger Prints Bureaux held atBhopal on 6-7 Jan 2011. All the Finger Print Experts from different State proposedto have National AFIS at NCRB, New Delhi. AFIS data centers should be hostedon State Data Center, so that there is a seamless integration of State AFISs toNational AFIS. As in times to come the average size of FP database will be nearly 10 lakhsor above for large States like Madhya Pradesh, Andra Pradesh, Tamilnadu & UttarPradesh etc. In similar manner 5 lakhs for small States like Kerala, Chhattisgarh,Jharkhand etc. So the proposed Database of AMBIS at NCRB should be: -  10 Digit Database- 1Crore with upgradibility up to 1.5 Crore  Chance Prints Database- 5lakh with upgradibility up to 10 lakh  Palm Print Database- 5lakh with upgradibility up to 10 lakh  Iris Database- 10 lacs with upgradibility up to 20 lakh  Face Database- 10 lacs with upgradibility up to 20 lakh Here we are considering these huge sizes of various databases in view ofCCTNS project in which all districts units as well as all Police Stations will beconnected through dedicated network. And as we know Fingerprint is an integralpart of CCTNS project.Core functionalities to be embedded in AMBIS  The system must perform reliable identification with large databases, as biometric identification systems tend to accumulate False Acceptance Rate (FAR) with database size increase and using a single fingerprint, face or iris image for identification becomes unreliable for a large-scale application. Several fingerprint images from persons different fingers or iris images from persons two eyes may be taken to increase matching reliability. Also, multi-biometric technologies (i.e. collecting fingerprint, face and / or iris samples from the same person) can be employed for greater reliability  The system must show high productivity and efficiency, which correspond to its scale: 3
  4. 4. 1. System scalability is important, as the system might be extended in the future, so a high productivity level should be kept by adding new units to the existing system. 2. The daily number of identification requests could be very high. 3. Identification requests should be processed in a very short time (ideally in real time), thus high computational power is required. 4. Support for large databases (tens or hundreds of millions of records) is required. 5. General system robustness. The system must be tolerant to hardware failures, as even temporary pauses in its work may cause big problems taking into account the application size.  The system must support major biometric standards. This should allow using the system-generated templates or databases with systems from other vendors and vice versa.  The system may need to match flat (plain) fingerprints with rolled fingerprints, as our department collect rolled fingerprint databases.  The system must be able to work in the network, as in most cases client workstations are remote from the server with the central database.  A forensic system must be able to edit latent fingerprint templates in order to submit latent fingerprints into the AFIS for the identification.Architecture of AMBISMulti-biometric systems can solve a number of problems of unimodal approaches.One source for such problems can be found in the lack of dynamic update of 4
  5. 5. parameters, which does not allow current systems to adapt to changes in theworking settings. They are generally calibrated once and for all, so that they aretuned and optimised with respect to standard conditions. In this work it is proposethat an architecture where, for each single-biometry subsystem, parameters aredynamically optimised according to the behaviour of all the others. This isachieved by an additional component, the supervisor module, which analyses theresponses from all subsystems and modifies the degree of reliability required fromeach of them to accept the respective responses. 5