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Paper Summary 3) Rank-level Fusion: After computing the match scores of each test signal, each biometric matcher (ECG or LDV) outputs a rank for each of the classes (enrolled individuals). We used techniques including the highest rank, Borda count, and the weighted Borda count (logistic regression) [5], to obtain the fused rank. 4) Decision-level Fusion: The decision made by a biometric matcher can be an accept or reject, in authentication mode, or an identity or reject, in identification mode. Example techniques we utilized to fuse the decisions from both biometric systems, include, ?AND? and ?OR? rules [6]. Fig. 1: A 5s segment of a subject?s simultaneously recorded ECG and LDV velocity waveforms. Result s: Using the score-level fusion techniques gave the best multibiometric performance improvement for both authentication and identification modes of operation. The authentication performance, as measured by EERs, improved from ~6% for each of the unimodal ECG and LDV biometric systems, to ~2% for the multibiometric system. Moreover, in the identification mode, the rank-1 accuracy improves from ~80% for each unimodal biometric system, to ~92% for the multibiometric system. Conclusions: We performed a large scale cardiovascular biometric experiment on 258 individuals from three sessions. This represents a significant extension of the results of uni-variate modeling [1], [2] to a multidimensional case. We performed a comprehensive study of the performance of the multibiometric system at different levels of the biometric pipeline, using standard fusion techniques. In one measure of performance, namely EERs, we showed that performance improves from about 5.3% to about 2.1%. Based on our analysis, we made recommendations on the methods that we found to work best in the authentication and identification modes of operation. References: [1] M. Chen, J. A. O?Sullivan, N. Singla, E. J. Sirevaag, S. D. Kristjansson, P.-H. Lai, A. D. Kaplan, and J. W. Rohrbaugh, ?Laser Doppler vibrometry measures of physiological function: evaluation of biometric capabilities,? TIFS, vol. 5, no. 3, pp. 449?460, 2010. [2] I. Odinaka, P.-H. Lai, A. D. Kaplan, J. A. O?Sullivan, E. J. Sirevaag, and J. W. Rohrbaugh, ?ECG biometric recognition: A comparative analysis,? TIFS, vol. 7, no. 6, pp. 1812?1824, Dec. 2012. [3] A. K. Jain and A. Ross, ?Multibiometric systems,? Commun. ACM, vol. 47, pp. 34?40, Jan. 2004. [4] A. Ross, K. Nandakumar, and A. K. Jain, Handbook of Multibiometrics. Springer-Verlag New York, Inc., 2006. [5] T. K. Ho, J. Hull, and S. Srihari, ?Decision combination in multiple classifier systems,? PAMI, vol. 16, no. 1, pp. 66?75, Jan 1994. [6] J. Daugman, ?Combining Multiple Biometrics.? http://www.cl.cam.ac.uk/_jgd1000/combine/combine.html, 2000.
Conference Report - BI OSI G 2016 The 15th edition of the International Conference of the Biometrics Special Interest Group (BIOSIG) took place at Fraunhofer IGD in Darmstadt, Germany from September 21 to 23 and attracted registered more than 100 participants. This year participants even travelled all the way from Japan, Korea, Argentina, Uruquay and the US to Darmstadt in order to join the BIOSIG community. The program was composed of scientific research contributions on the one hand and reports about large-scale applications on the other hand. The opening keynote talk was given by Richard Rinkens (EC, DG Home), who presented the new proposal for the Entry Exit System (EES), adopted by the European Commission after an intense period of analysis and real-world testing (eu-LISA has tested 13 entries - sea, rail, air - with different options). The new proposal is based on multi-modal biometrics combining 4-fingerprints and a good quality facial-image taken live. In particular, new procedures for EU citizens, 3rd country nationals with visa and 3rd country without visa are considered. One of the main objectives is the identification of overstayers and to enable subsequent evidence based visa policy making. To tackle these issues, the EES will substitute manual procedures with electronic stamps in an electronic database. In addition, a new link has been made between EES and VIS, and a common AFIS should be deployed for the three systems. Future features include a webservice to check credentials and authorized time left, which will be the first consultable service for an EU system of this kind through internet. Finally, it was highlighted that the EES will seriously reinforce internal security and fight against terrorism. Accepted conference contributions included 16 presentations and covering soft biometrics for face or iris, fingerprint, vein, face, iris, voice, gait and keystroke recognition. Other challenges addressed included facial forensics use cases, voice activity segmentation, 3D gloves for contactless verification, new biometric template protection schemes or the generation of synthetic fingerprint alteration database to allow the further development of algorithms to detect them. Another relevant topic covered in the conference was Presentation Attack Detection (PAD) for characteristics such as face and iris. The poster session with 25 contributions was a good mix of research results from academic and industrial research labs and visitors did spend a long time in the poster exhibition before the start of the social event with the traditional late summer barbeque ? providing lots of opportunities for networking. On the last day of the conference, Arun Ross addressed the issue of personal privacy in biometric systems. In particular, he considered several questions such as (a) Can additional information about an individual be automatically gleaned from biometric data? (b) Can biometrics be used to surreptitiously track an individual? (c) Can multiple biometric databases be linked to develop a more complete profile of an individual? (d) Who owns the biometric data collected from an individual, and how long should the biometric data be retained in an identity management system? He started the keynote with a definition stemming from 1890: ?privacy is the right to be left alone?. However, in contemporary society it is easy for someone to take a picture of you and find out who you are by harnessing the power of automated face recognition and cloud computing. In the first part of the talk, he discussed methods by which biographical (e.g., gender from fingerprints), anatomical (e.g., crypts in iris), sensorial (e.g., type of device used to image a certain fingerprint), and environmental (e.g., intensity of ambient lighting during iris image acquisition) information can be gleaned from biometric data.
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