1. A method for classifying biometric data ( 1 ) 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 ( 1 ) is represented by a first distribution ( 2 ) 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 ( 3 ) in automatic classification of a universal collection ( 11 ) 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 ( 31 ), 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 ( 11 ) of biometric data, the second set of second parameters being optimized by a first implementation ( 7 ) 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 ( 2 ) corresponding to said universal collection of biometric data, the first statistical model ( 3 ) thus being able to match any biometric data ( 1 ) 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 ( 4 ) comprising a plurality of statistical sub-models ( 41 ) in automatic classification of a set of first individual collections ( 12 ) 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 ( 12 ), the determination of a sub-space corresponding thereto according to the first statistical model ( 3 ), and in this sub-space, its classification ( 8 ) in the corresponding sub-model, such that each sub-model ( 41 ) is defined by a third set of first distributions ( 2 ), the second construction step further comprising, for each sub-space, a step of selecting ( 9 ) 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 ( 21 ) of first distributions ( 2 ) maximizing the coverage of the sub-space, such that each sub-model ( 41 ) is defined by the fourth minimum set ( 21 ) of first distributions corresponding thereto, such that the plurality of statistical sub-models ( 41 ) of the second statistical model ( 4 ) is defined by a plurality of fourth minimum sets of first distributions, the second statistical model ( 4 ) being thus able to make it possible for any biometric data of a determined sub-space according to the first statistical model ( 3 ), to determine the first distribution with which its likelihood is maximum from amongst the first distributions of the fourth minimum set ( 21 ) 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.