Thierry Brouard

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We propose a new hybrid protocol for cryptographically secure biometric authentication. The main advantages of the proposed protocol over previous solutions can be summarised as follows: (1) potential for much better accuracy using different types of biometric signals, including behavioural ones; and (2) improved user privacy, since user identities are not(More)
We present a new approach for recognition of complex graphic symbols in technical documents. Graphic symbol recognition is a well known challenge in the field of document image analysis and is at heart of most graphic recognition systems. Our method uses structural approach for symbol representation and statistical classifier for symbol recognition. In our(More)
Structural pattern recognition approaches offer the most expressive, convenient, powerful but computational expensive representations of underlying relational information. To benefit from mature, less expensive and efficient state-of-the-art machine learning models of statistical pattern recognition they must be mapped to a low-dimensional vector space. Our(More)
This paper describes two approaches based on evolutionary algorithms for determining Bayesian networks structures from a database of cases. One major difficulty when tackling the problem of structure learning with evolutionary strategies is to avoid the premature convergence of the population to a local optimum. In this paper, we propose two methods in(More)
Different features carry more or less rich and varied pieces of information to characterize a pattern. The fusion of these different sources of information can provide an opportunity to develop more efficient biometric system compared when using a feature vector. Thus a new automatic fusion methodology using different sources of information (different(More)
In this paper, we are dealing with color object tracking. We propose to use Hidden Markov Models in a different way as classical approaches. Indeed, we use these mathematical tools to model the object in the spatial domain rather than in the temporal domain. Besides in order to manage multidimensional (color) data, Multidimensional Hidden Markov Models are(More)
We present a method for spotting a subgraph in a graph repository. Subgraph spotting is a very interesting research problem for various application domains where the use of a relational data structure is mandatory. Our proposed method accomplishes subgraph spotting through graph embedding. We achieve automatic indexation of a graph repository during(More)
We present a content spotting system for line drawing graphic document images. The proposed system is sufficiently domain independent and takes the keyword based information retrieval for graphic documents, one step forward, to Query By Example (QBE) and focused retrieval. During offline learning mode: we vectorize the documents in the repository, represent(More)