Quality-driven and real-time iris recognition from close-up eye videos

International audienceThis paper deals with the computation of robust iris templates from video sequences. The main contribution is to propose (i) optimal tracking and robust detection of the pupil, (ii) smart selection of iris images to be enrolled, and (iii) multi-thread and quality-driven decomposition of tasks to reach real-time processing. The evaluation of the system was done on the Multiple Biometric Grand Challenge dataset. Especially we conducted a systematic study regarding the fragile bit rate and the number of merged images, using classical criteria. We reached an equal error rate value of 0.2% which reflects high performance on this database with respect to previous studies

Robust and efficient Fourier-Mellin transform approximations for invariant grey-level image description and reconstruction

International audienceThis paper addresses the gray-level image representation ability of the Fourier-Mellin Transform (FMT) for pattern recognition, reconstruction and image database retrieval. The main practical di±culty of the FMT lies in the accuracy and e±ciency of its numerical approximation and we propose three estimations of its analytical extension. Comparison of these approximations is performed from discrete and ¯nite-extent sets of Fourier- Mellin harmonics by means of experiments in: (i) image reconstruction via both visual inspection and the computation of a reconstruction error; and (ii) pattern recognition and discrimination by using a complete and convergent set of features invariant under planar similarities. Experimental results on real gray-level images show that it is possible to recover an image to within a speci¯ed degree of accuracy and to classify objects reliably even when a large set of descriptors is used. Finally, an example will be given, illustrating both theoretical and numerical results in the context of content-based image retrieval

Robust partial-learning in linear Gaussian systems

International audienceThis paper deals with unsupervised and off-line learning of parameters involved in linear Gaussian systems, i.e. the estimation of the transition and the noise covariances matrices of a state-space system from a finite series of observations only. In practice, these systems are the result of a physical problem for which there is a partial knowledge either on the sensors from which the observations are issued or on the state of the studied system. We therefore propose in this work an " Expectation-Maximization " learning type algorithm that takes into account constraints on parameters such as the fact that two identical sensors have the same noise characteristics, and so estimation procedure should exploit this knowledge. The algorithms are designed for the pairwise linear Gaussian system that takes into account supplementary cross-dependences between observations and hidden states w.r.t. the conventional linear system, while still allowing optimal filtering by means of a Kalman-like filter. The algorithm is made robust through QR decompositions and the propagation of a square-root of the covariance matrices instead of the matrices themselves. It is assessed through a series of experiments that compare the algorithms which incorporate or not partial knowledge, for short as well as for long signals

Fast exact filtering in generalized conditionally observed Markov switching models with copulas

International audienceWe deal with the problem of statistical filtering in the context of Markov switching models. For X_1^N hidden continuous process, R_1^N hidden finite Markov process, and Y_1^N observed continuous one, the problem is to sequentially estimate X_1^N and R_1^N from Y_1^N. In the classical " conditional Gaussian Linear state space model " (CGLSSM), where (R_1^N, X_1^N) is a hidden Gaussian Markov chain, fast exact filtering is not workable. Recently, " conditionally Gaussian observed Markov switching model " (CGOMSM) has been proposed, in which (R_1^N, Y_1^N) is a hidden Gaussian Markov chain instead. This model allows fast exact filtering. In this paper, using copula, we extend CGOMSM to a more general one, in which (R_1^N, Y_1^N) is a hidden Markov chain (HMC) with noise of any form and the regimes are no need to be all Gaussian, while the exact filtering is still workable. Experiments are conducted to show how the exact filtering results based on CGOMSM can be improved by the use of the new model

Fixed-gaze head movement detection for triggering commands

International audienceIn the field of human-computer interaction, mobile eye-tracking devices (carried on a pair of glasses) can be used to interact with an object remotely, in mobility, while keeping both hands free to perform the main activity. This process allows us to interact with objects beyond our reach and sometimes it can be faster than a traditional interaction. On the other hand, as the main task of the eyes is to observe the environment, it becomes difficult to differentiate a simple observation of an object in the scene from the will to interact with it (Midas touch). To solve this problem, solutions have been proposed in the literature, such as the use of voluntary eye movements, the use of smooth pursuit, or coupling the eye-tracking with a secondary device. In this context, our study focuses on the analysis of voluntary head movement when the user's eyes are fixed on the object of interest to trigger various commands. In order to evaluate the appropriateness of this approach in realistic situation and to evaluate its performance, we have conducted a test of the detection of 6 different fixed-gaze head movements on 40 people: head shaking (right, left), nodding (up, down) and tilting (right, left). During this test, we asked the participants to learn quickly these six movements, then to trigger various commands using these movements. The success rate is 70%, but this rate depends on the individuals and the gestures performed. As these movements are rarely used during the observation of an object, the problem of Midas touch can be avoided, while keeping both hands free

Lower limb locomotion activity recognition of healthy individuals using semi-Markov model and single wearable inertial sensor

International audienceLower limb locomotion activity is of great interest in the field of human activity recognition. In this work, a triplet semi-Markov model-based method is proposed to recognize the locomotion activities of healthy individuals when lower limbs move periodically. In the proposed algorithm, the gait phases (or leg phases) are introduced into the hidden states, and Gaussian mixture density is introduced to represent the complex conditioned observation density. The introduced sojourn state forms the semi-Markov structure, which naturally replicates the real transition of activity and gait during motion. Then, batch mode and on-line Expectation-Maximization (EM) algorithms are proposed, respectively, for model training and adaptive on-line recognition. The algorithm is tested on two datasets collected from wearable inertial sensors. The batch mode recognition accuracy reaches up to 95.16%, whereas the adaptive on-line recognition gradually obtains high accuracy after the time required for model updating. Experimental results show an improvement in performance compared to the other competitive algorithm

Using Fourier-based shape alignment to add geometric prior to snakes

International audienceIn this paper, we present a new algorithm of snakes with geometric prior. A method of shape alignment using Fourier coefficients is introduced to estimate the Euclidean transformation between the evolving snake and a template of the searched object. This allows the definition of a new field of forces making the evolving snake to have a shape similar to the template one. Furthermore, this strategy can be used to manage several possible templates by computing a shape distance to select the best one at each iteration. The new method also solves some well-known limitations of snakes such as evolution in concave boundaries, and enhances the robustness to noise and partially occluded objects. A series of experimental results is presented to illustrate performances

Fourier-based geometric shape prior for snakes

International audienceA novel method of snakes with shape prior is presented in this paper. We propose to add a new force which makes the curve evolve to particular shape corresponding to a template to overcome some well-known problems of snakes. The template is an instance or a sketch of the researched contour without knowing its exact geometric pose in the image. The prior information is introduced through a set of complete and locally stable invariants to Euclidean transformations (translation, rotation and scale factor) computed using Fourier Transform on contours. The method is evaluated with the segmentation of myocardial scintigraphy slices and the tracking of an object in a video sequence

Fast smoothing in switching approximations of non-linear and non-Gaussian models

International audienceStatistical smoothing in general non-linear non-Gaussian systems is a challenging problem. A new smoothing method based on approximating the original system by a recent switching model has been introduced. Such switching model allows fast and optimal smoothing. The new algorithm is validated through an application on stochastic volatility and dynamic beta models. Simulation experiments indicate its remarkable performances and low processing cost. In practice, the proposed approach can overcome the limitations of particle smoothing methods and may apply where their usage is discarded

Transformée de Fourier-Mellin numérique : Reconstruction et estimation de mouvement d'objets à niveaux de gris

La transformée de Fourier-Mellin est utilisée pour estimer les paramètres de mouvement relatifs entre des objets à niveaux de gris de même forme mais de taille et d'orientation différentes. Nous définissons une énergie basée sur la distance euclidienne exprimée sur l'espace de Fourier-Mellin et le théorème du retard appliqué au groupe des similitudes vectorielles. La localisation du minimum de cette énergie fournie une estimation des paramètres de mouvement. Les résultats expérimentaux confirment la robustesse des différentes approximations numériques du spectre de Fourier-Mellin et la fiabilité des paramètres de mouvement estimés