A Swarm intelligence approach for biometrics verification and identification

In this paper we investigate a swarm intelligence classification approach for both biometrics verification and identification problems. We model the problem by representing biometric templates as ants, grouped in colonies representing the clients of a biometrics authentication system. The biometric template classification process is modeled as the aggregation of ants to colonies. When test input data is captured -- a new ant in our representation -- it will be influenced by the deposited phermonones related to the population of the colonies. We experiment with the Aggregation Pheromone density based Classifier (APC), and our results show that APC outperforms ``traditional'' techniques -- like 1-nearest-neighbour and Support Vector Machines -- and we also show that performance of APC are comparable to several state of the art face verification algorithms. The results here presented let us conclude that swarm intelligence approaches represent a very promising direction for further investigations for biometrics verification and identification

On the use of SIFT features for face authentication

Several pattern recognition and classification techniques have been applied to the biometrics domain. Among them, an interesting technique is the Scale Invariant Feature Transform (SIFT), originally devised for object recognition. Even if SIFT features have emerged as a very powerful image descriptors, their employment in face analysis context has never been systematically investigated. This paper investigates the application of the SIFT approach in the context of face authentication. In order to determine the real potential and applicability of the method, different matching schemes are proposed and tested using the BANCA database and protocol, showing promising results

Genetic Programming for Multibiometrics

Biometric systems suffer from some drawbacks: a biometric system can provide in general good performances except with some individuals as its performance depends highly on the quality of the capture. One solution to solve some of these problems is to use multibiometrics where different biometric systems are combined together (multiple captures of the same biometric modality, multiple feature extraction algorithms, multiple biometric modalities...). In this paper, we are interested in score level fusion functions application (i.e., we use a multibiometric authentication scheme which accept or deny the claimant for using an application). In the state of the art, the weighted sum of scores (which is a linear classifier) and the use of an SVM (which is a non linear classifier) provided by different biometric systems provide one of the best performances. We present a new method based on the use of genetic programming giving similar or better performances (depending on the complexity of the database). We derive a score fusion function by assembling some classical primitives functions (+, *, -, ...). We have validated the proposed method on three significant biometric benchmark datasets from the state of the art

Fast computation of the performance evaluation of biometric systems: application to multibiometric

The performance evaluation of biometric systems is a crucial step when designing and evaluating such systems. The evaluation process uses the Equal Error Rate (EER) metric proposed by the International Organization for Standardization (ISO/IEC). The EER metric is a powerful metric which allows easily comparing and evaluating biometric systems. However, the computation time of the EER is, most of the time, very intensive. In this paper, we propose a fast method which computes an approximated value of the EER. We illustrate the benefit of the proposed method on two applications: the computing of non parametric confidence intervals and the use of genetic algorithms to compute the parameters of fusion functions. Experimental results show the superiority of the proposed EER approximation method in term of computing time, and the interest of its use to reduce the learning of parameters with genetic algorithms. The proposed method opens new perspectives for the development of secure multibiometrics systems by speeding up their computation time.Comment: Future Generation Computer Systems (2012

Face Video Competition

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-01793-3_73Person recognition using facial features, e.g., mug-shot images, has long been used in identity documents. However, due to the widespread use of web-cams and mobile devices embedded with a camera, it is now possible to realise facial video recognition, rather than resorting to just still images. In fact, facial video recognition offers many advantages over still image recognition; these include the potential of boosting the system accuracy and deterring spoof attacks. This paper presents the first known benchmarking effort of person identity verification using facial video data. The evaluation involves 18 systems submitted by seven academic institutes.The work of NPoh is supported by the advanced researcher fellowship PA0022121477of the Swiss NSF; NPoh, CHC and JK by the EU-funded Mobio project grant IST-214324; NPC and HF by the EPSRC grants EP/D056942 and EP/D054818; VS andNP by the Slovenian national research program P2-0250(C) Metrology and Biomet-ric System, the COST Action 2101 and FP7-217762 HIDE; and, AAS by the Dutch BRICKS/BSIK project.Poh, N.; Chan, C.; Kittler, J.; Marcel, S.; Mc Cool, C.; Rua, E.; Alba Castro, J.... (2009). Face Video Competition. En Advances in Biometrics: Third International Conference, ICB 2009, Alghero, Italy, June 2-5, 2009. Proceedings. 715-724. https://doi.org/10.1007/978-3-642-01793-3_73S715724Messer, K., Kittler, J., Sadeghi, M., Hamouz, M., Kostyn, A., Marcel, S., Bengio, S., Cardinaux, F., Sanderson, C., Poh, N., Rodriguez, Y., Kryszczuk, K., Czyz, J., Vandendorpe, L., Ng, J., Cheung, H., Tang, B.: Face authentication competition on the BANCA database. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 8–15. Springer, Heidelberg (2004)Messer, K., Kittler, J., Sadeghi, M., Hamouz, M., Kostin, A., Cardinaux, F., Marcel, S., Bengio, S., Sanderson, C., Poh, N., Rodriguez, Y., Czyz, J., Vandendorpe, L., McCool, C., Lowther, S., Sridharan, S., Chandran, V., Palacios, R.P., Vidal, E., Bai, L., Shen, L.-L., Wang, Y., Yueh-Hsuan, C., Liu, H.-C., Hung, Y.-P., Heinrichs, A., Muller, M., Tewes, A., vd Malsburg, C., Wurtz, R., Wang, Z., Xue, F., Ma, Y., Yang, Q., Fang, C., Ding, X., Lucey, S., Goss, R., Schneiderman, H.: Face authentication test on the BANCA database. In: Int’l. Conf. Pattern Recognition (ICPR), vol. 4, pp. 523–532 (2004)Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the Face Recognition Grand Challenge. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 947–954 (2005)Bailly-Baillière, E., Bengio, S., Bimbot, F., Hamouz, M., Kittler, J., Marithoz, J., Matas, J., Messer, K., Popovici, V., Porée, F., Ruiz, B., Thiran, J.-P.: The BANCA Database and Evaluation Protocol. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688. Springer, Heidelberg (2003)Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)Martin, A., Doddington, G., Kamm, T., Ordowsk, M., Przybocki, M.: The DET Curve in Assessment of Detection Task Performance. In: Proc. Eurospeech 1997, Rhodes, pp. 1895–1898 (1997)Bengio, S., Marithoz, J.: The Expected Performance Curve: a New Assessment Measure for Person Authentication. In: The Speaker and Language Recognition Workshop (Odyssey), Toledo, pp. 279–284 (2004)Poh, N., Bengio, S.: Database, Protocol and Tools for Evaluating Score-Level Fusion Algorithms in Biometric Authentication. Pattern Recognition 39(2), 223–233 (2005)Martin, A., Przybocki, M., Campbell, J.P.: The NIST Speaker Recognition Evaluation Program, ch. 8. Springer, Heidelberg (2005

Face verification system architecture using smart cards

A smart card based face verification system is pro-posed in which the feature extraction and decision mak-ing is performed on the card. Such an architecture has many privacy and security benefits. As smart cards are limited computational platforms, the face verifica-tion algorithms have to be adapted to limit the facial image representations. This minimises the information needed to be sent to the card and lessens the computa-tional load of the template matching. Studies performed on the BANCA and XM2VTS databases demonstrate that by limiting these representations the verification perfor-mance of the system is not degraded and that the pro-posed architecture is a viable one. 1

On accuracy/robustness/complexity trade-offs in face verification

Copyright © 2005 IEEEIn much of the literature devoted to face recognition, experiments are performed with controlled images (e.g. manual face localization, controlled lighting, background and pose). However, a practical recognition system has to be robust to more challenging conditions. In this paper we first evaluate, on the relatively difficult BANCA database, the discrimination accuracy, robustness and complexity of Gaussian Mixture Model (GMM), 1D- and pseudo-2D Hidden Markov Model (HMM) based systems, using both manual and automatic face localization. We also propose to extend the GMM approach through the use of local features with embedded positional information, increasing accuracy without sacrificing its low complexity. Experiments show that good accuracy on manually located faces is not necessarily indicative of good accuracy on automatically located faces (which are imperfectly located). The deciding factor is shown to be the degree of constraints placed on spatial relations between face parts. Methods which utilize rigid constraints have poor robustness compared to methods which have relaxed constraints. Furthermore, we show that while the pseudo-2D HMM approach has the best overall accuracy, classification time on current hardware makes it impractical. The best trade-off in terms of complexity, robustness and discrimination accuracy is achieved by the extended GMM approach.Conrad Sanderson, Fabien Cardinaux, Samy Bengi

A Survey on Ear Biometrics

Recognizing people by their ear has recently received significant attention in the literature. Several reasons account for this trend: first, ear recognition does not suffer from some problems associated with other non contact biometrics, such as face recognition; second, it is the most promising candidate for combination with the face in the context of multi-pose face recognition; and third, the ear can be used for human recognition in surveillance videos where the face may be occluded completely or in part. Further, the ear appears to degrade little with age. Even though, current ear detection and recognition systems have reached a certain level of maturity, their success is limited to controlled indoor conditions. In addition to variation in illumination, other open research problems include hair occlusion; earprint forensics; ear symmetry; ear classification; and ear individuality. This paper provides a detailed survey of research conducted in ear detection and recognition. It provides an up-to-date review of the existing literature revealing the current state-of-art for not only those who are working in this area but also for those who might exploit this new approach. Furthermore, it offers insights into some unsolved ear recognition problems as well as ear databases available for researchers