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)
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)
1 Abstract 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(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, Multi-dimensional Hidden Markov Models are(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,(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)
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)
We present a new method for explicit graph embedding. Our algorithm extracts a feature vector for an undirected attributed graph. The proposed feature vector encodes details about the number of nodes, number of edges, node degrees, the attributes of nodes and the attributes of edges in the graph. The first two features are for the number of nodes and the(More)