1. A method for classifying biometric data belonging to a first continuous and limited area of representation corresponding to at least one determined type biometric sensor, comprising: a step of acquiring by said at least one sensor of determined type biometric data in the form of a set of multidimensional vectors of parameters, a step of defining a second area of representation wherein each biometric data is represented by a first distribution such that the biometric data are represented thereto by a first set of first distributions, the first distributions responding to a same probability distribution function of at least one first parameter, such that the first set of first distributions is defined by a first set of first parameters determined based on biometric data, a first step of constructing a first statistical model in automatic classification of a universal collection of biometric data in the second area of representation, the first statistical model being defined by at least one second finite set of second distributions, the second distributions responding to a same probability distribution function of at least one second parameter and being distributed in the second area of representation according to at least one second set of second parameters, such that each second distribution defines a sub-space of the second area of representation comprising a plurality of first distributions representative of a part of said universal collection of biometric data, the second set of second parameters being optimized by a first implementation of a likelihood maximization algorithm, such that the second finite set of second distributions represents with the maximum likelihood the first set of first distributions corresponding to said universal collection of biometric data, the first statistical model thus being able to match any biometric data with a determined sub-space, each sub-space being associated with a plurality of biometric data, and a second step of constructing a second statistical model comprising a plurality of statistical sub-models in automatic classification of a set of first individual collections of biometric data, each sub-model being defined in a corresponding sub-space of the second area of representation, the second construction step comprising, for each distribution representative of each biometric data of each individual collection, the determination of a sub-space corresponding thereto according to the first statistical model, and in this sub-space, its classification in the corresponding sub-model, such that each sub-model is defined by a third set of first distributions, the second construction step further comprising, for each sub-space, a step of selecting first distributions amongst said third set of first distributions by implementing a coverage maximization and redundancy minimization iterative algorithm, for determining a fourth minimum set of first distributions maximizing the coverage of the sub-space, such that each sub-model is defined by the fourth minimum set of first distributions corresponding thereto, such that the plurality of statistical sub-models of the second statistical model is defined by a plurality of fourth minimum sets of first distributions, the second statistical model being thus able to make it possible for any biometric data of a determined sub-space according to the first statistical model, to determine the first distribution with which its likelihood is maximum from amongst the first distributions of the fourth minimum set of first corresponding distributions, a step of saving to a memorization medium first and second statistical models, such as to make their future use in automatic classification of biometric data possible.
1. A method for classifying biometric data belonging to a first continuous and limited area of representation corresponding to at least one determined type biometric sensor, comprising: a step of acquiring by said at least one sensor of determined type biometric data in the form of a set of multidimensional vectors of parameters, a step of defining a second area of representation wherein each biometric data is represented by a first distribution such that the biometric data are represented thereto by a first set of first distributions, the first distributions responding to a same probability distribution function of at least one first parameter, such that the first set of first distributions is defined by a first set of first parameters determined based on biometric data, a first step of constructing a first statistical model in automatic classification of a universal collection of biometric data in the second area of representation, the first statistical model being defined by at least one second finite set of second distributions, the second distributions responding to a same probability distribution function of at least one second parameter and being distributed in the second area of representation according to at least one second set of second parameters, such that each second distribution defines a sub-space of the second area of representation comprising a plurality of first distributions representative of a part of said universal collection of biometric data, the second set of second parameters being optimized by a first implementation of a likelihood maximization algorithm, such that the second finite set of second distributions represents with the maximum likelihood the first set of first distributions corresponding to said universal collection of biometric data, the first statistical model thus being able to match any biometric data with a determined sub-space, each sub-space being associated with a plurality of biometric data, and a second step of constructing a second statistical model comprising a plurality of statistical sub-models in automatic classification of a set of first individual collections of biometric data, each sub-model being defined in a corresponding sub-space of the second area of representation, the second construction step comprising, for each distribution representative of each biometric data of each individual collection, the determination of a sub-space corresponding thereto according to the first statistical model, and in this sub-space, its classification in the corresponding sub-model, such that each sub-model is defined by a third set of first distributions, the second construction step further comprising, for each sub-space, a step of selecting first distributions amongst said third set of first distributions by implementing a coverage maximization and redundancy minimization iterative algorithm, for determining a fourth minimum set of first distributions maximizing the coverage of the sub-space, such that each sub-model is defined by the fourth minimum set of first distributions corresponding thereto, such that the plurality of statistical sub-models of the second statistical model is defined by a plurality of fourth minimum sets of first distributions, the second statistical model being thus able to make it possible for any biometric data of a determined sub-space according to the first statistical model, to determine the first distribution with which its likelihood is maximum from amongst the first distributions of the fourth minimum set of first corresponding distributions, a step of saving to a memorization medium first and second statistical models, such as to make their future use in automatic classification of biometric data possible. 7. The method according to claim 1 , wherein the first statistical model is a Gaussian mixture model defined by the second finite set of second distributions according to the sets of parameters whereof a set of means, each associated with a biometric data, and a set of standard deviations, each associated with a biometric data, the first implementation consisting in implementing the likelihood maximization algorithm called expectation-maximization algorithm according to a criterion of determined maximum likelihood.