Biologically inspired speaker verification

Speaker verification is an active research problem that has been addressed using a variety of different classification techniques. However, in general, methods inspired by the human auditory system tend to show better verification performance than other methods. In this thesis three biologically inspired speaker verification algorithms are presented

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

Securing Cloud Storage by Transparent Biometric Cryptography

With the capability of storing huge volumes of data over the Internet, cloud storage has become a popular and desirable service for individuals and enterprises. The security issues, nevertheless, have been the intense debate within the cloud community. Significant attacks can be taken place, the most common being guessing the (poor) passwords. Given weaknesses with verification credentials, malicious attacks have happened across a variety of well-known storage services (i.e. Dropbox and Google Drive) – resulting in loss the privacy and confidentiality of files. Whilst today's use of third-party cryptographic applications can independently encrypt data, it arguably places a significant burden upon the user in terms of manually ciphering/deciphering each file and administering numerous keys in addition to the login password. The field of biometric cryptography applies biometric modalities within cryptography to produce robust bio-crypto keys without having to remember them. There are, nonetheless, still specific flaws associated with the security of the established bio-crypto key and its usability. Users currently should present their biometric modalities intrusively each time a file needs to be encrypted/decrypted – thus leading to cumbersomeness and inconvenience while throughout usage. Transparent biometrics seeks to eliminate the explicit interaction for verification and thereby remove the user inconvenience. However, the application of transparent biometric within bio-cryptography can increase the variability of the biometric sample leading to further challenges on reproducing the bio-crypto key. An innovative bio-cryptographic approach is developed to non-intrusively encrypt/decrypt data by a bio-crypto key established from transparent biometrics on the fly without storing it somewhere using a backpropagation neural network. This approach seeks to handle the shortcomings of the password login, and concurrently removes the usability issues of the third-party cryptographic applications – thus enabling a more secure and usable user-oriented level of encryption to reinforce the security controls within cloud-based storage. The challenge represents the ability of the innovative bio-cryptographic approach to generate a reproducible bio-crypto key by selective transparent biometric modalities including fingerprint, face and keystrokes which are inherently noisier than their traditional counterparts. Accordingly, sets of experiments using functional and practical datasets reflecting a transparent and unconstrained sample collection are conducted to determine the reliability of creating a non-intrusive and repeatable bio-crypto key of a 256-bit length. With numerous samples being acquired in a non-intrusive fashion, the system would be spontaneously able to capture 6 samples within minute window of time. There is a possibility then to trade-off the false rejection against the false acceptance to tackle the high error, as long as the correct key can be generated via at least one successful sample. As such, the experiments demonstrate that a correct key can be generated to the genuine user once a minute and the average FAR was 0.9%, 0.06%, and 0.06% for fingerprint, face, and keystrokes respectively. For further reinforcing the effectiveness of the key generation approach, other sets of experiments are also implemented to determine what impact the multibiometric approach would have upon the performance at the feature phase versus the matching phase. Holistically, the multibiometric key generation approach demonstrates the superiority in generating the bio-crypto key of a 256-bit in comparison with the single biometric approach. In particular, the feature-level fusion outperforms the matching-level fusion at producing the valid correct key with limited illegitimacy attempts in compromising it – 0.02% FAR rate overall. Accordingly, the thesis proposes an innovative bio-cryptosystem architecture by which cloud-independent encryption is provided to protect the users' personal data in a more reliable and usable fashion using non-intrusive multimodal biometrics.Higher Committee of Education Development in Iraq (HCED

Speaker Recognition in the Biometric Security Systems

At present, the importance of the biometric security increases a lot in context of the events in the world. Development of the individual biometric technologies such as the fingerprint recognition, iris or retina recognition or speaker recognition has been considered very important. However, it comes to be true that only one biometric technology is not sufficient enough. One of the most prosperous solutions might be a combination of more such technologies. This article aims at the technology of the speaker recognition and proposes a solution of its integration into a more complex biometric security system. Herein a design of the complex biometric security system is introduced based on the speaker recognition and the fingerprint authentication. A method of acquisition of a unique vector from speaker specific features is introduced as well

Verificación de firmas en línea usando modelos de mezcla Gaussianas y estrategias de aprendizaje para conjuntos pequeños de muestras

RESUMEN: El artículo aborda el problema de entrenamiento de sistemas de verificación de firmas en línea cuando el número de muestras disponibles para el entrenamiento es bajo, debido a que en la mayoría de situaciones reales el número de firmas disponibles por usuario es muy limitado. El artículo evalúa nueve diferentes estrategias de clasificación basadas en modelos de mezclas de Gaussianas (GMM por sus siglas en inglés) y la estrategia conocida como modelo histórico universal (UBM por sus siglas en inglés), la cual está diseñada con el objetivo de trabajar bajo condiciones de menor número de muestras. Las estrategias de aprendizaje de los GMM incluyen el algoritmo convencional de Esperanza y Maximización, y una aproximación Bayesiana basada en aprendizaje variacional. Las firmas son caracterizadas principalmente en términos de velocidades y aceleraciones de los patrones de escritura a mano de los usuarios. Los resultados muestran que cuando se evalúa el sistema en una configuración genuino vs. impostor, el método GMM-UBM es capaz de mantener una precisión por encima del 93%, incluso en casos en los que únicamente se usa para entrenamiento el 20% de las muestras disponibles (equivalente a 5 firmas), mientras que la combinación de un modelo Bayesiano UBM con una Máquina de Soporte Vectorial (SVM por sus siglas en inglés), modelo conocido como GMM-Supervector, logra un 99% de acierto cuando las muestras de entrenamiento exceden las 20. Por otro lado, cuando se simula un ambiente real en el que no están disponibles muestras impostoras y se usa únicamente el 20% de las muestras para el entrenamiento, una vez más la combinación del modelo UBM Bayesiano y una SVM alcanza más del 77% de acierto, manteniendo una tasa de falsa aceptación inferior al 3%.ABSTRACT: This paper addresses the problem of training on-line signature verification systems when the number of training samples is small, facing the real-world scenario when the number of available signatures per user is limited. The paper evaluates nine different classification strategies based on Gaussian Mixture Models (GMM), and the Universal Background Model (UBM) strategy, which are designed to work under small-sample size conditions. The GMM’s learning strategies include the conventional Expectation-Maximisation algorithm and also a Bayesian approach based on variational learning. The signatures are characterised mainly in terms of velocities and accelerations of the users’ handwriting patterns. The results show that for a genuine vs. impostor test, the GMM-UBM method is able to keep the accuracy above 93%, even when only 20% of samples are used for training (5 signatures). Moreover, the combination of a full Bayesian UBM and a Support Vector Machine (SVM) (known as GMM-Supervector) is able to achieve 99% of accuracy when the training samples exceed 20. On the other hand, when simulating a real environment where there are not available impostor signatures, once again the combination of a full Bayesian UBM and a SVM, achieve more than 77% of accuracy and a false acceptance rate lower than 3%, using only 20% of the samples for training