Method of detecting cast shadows on an image of size '0', said method comprising: - a learning phase in the course of which each image of a series of test images undergoes a linear discriminant analysis in order to determine the characteristics of the "cast shadows" class and is distributed into "cast shadows" or "not cast shadows" classes, - a capture step in the course of which an acquisition device captures and optionally redimensions the image of size '0' of the face of an individual so that the image of size '0' exhibits a definition of n*n where n = 2 N and where N is a natural integer greater than or equal to 2, for 'p' varying from 0 to N-1 - a cutting step in the course of which the image of size 'p' (100, 200) is cut into n P *n p pixels where n p = 2 ( N-p), - a first step of normalization of the image of size 'p' (100, 200), in the course of which the value X ij p of each pixel of the image of size 'p' (100, 200) is normalized in the form of a value x ij p = X ij p ∑ i = 1 n ∑ j = 1 n X ij p 2 - a computation step in the course of which the variance σ p of the image of size 'p' is computed, - a reduction step in the course of which the resolution of the image of size 'p' is reduced by a factor of 2 into an image of size 'p+1', where the values X ij p + 1 of the pixels of the image of size 'p+1' are computed with respect to the normalized values x ij p of the pixels of the image of size 'p', - an incrementation step in the course of which the index 'p' is incremented by 1, - a test step in the course of which the value of the index 'p' is compared with N, - if the result of the test step is negative, a looping step in the course of which the method of detection loops back to the cutting step, - a second normalization step in the course of which the variance σ p of each reduced image of size 'p' where p varies from 1 to N-1 is divided by the variance σ 0 of the image of size '0', - a vectorization step, in the course of which a vector of characteristics is constructed on the basis of the set of variances thus normalized σ 1 / σ 0, σ 2 / σ 0,..., σ N -1 / σ 0, - a multiplication step in the course of which is computed the scalar product of the vector of characteristics and of the predetermined eigenvector arising from the LDA, - a comparison step in the course of which the scalar product thus computed is compared with a reference threshold value, and - a classification step in the course of which the image of size '0' is classed into the "cast shadows" class if the score is greater than the reference threshold value, and is not classed into the "cast shadows" class if the score is less than the reference threshold value. - a learning phase in the course of which each image of a series of test images undergoes a linear discriminant analysis in order to determine the characteristics of the "cast shadows" class and is distributed into "cast shadows" or "not cast shadows" classes, - a capture step in the course of which an acquisition device captures and optionally redimensions the image of size '0' of the face of an individual so that the image of size '0' exhibits a definition of n*n where n = 2 N and where N is a natural integer greater than or equal to 2, for 'p' varying from 0 to N-1 - a cutting step in the course of which the image of size 'p' (100, 200) is cut into n P *n p pixels where n p = 2 ( N-p), - a first step of normalization of the image of size 'p' (100, 200), in the course of which the value X ij p of each pixel of the image of size 'p' (100, 200) is normalized in the form of a value x ij p = X ij p ∑ i = 1 n ∑ j = 1 n X ij p 2 - a computation step in the course of which the variance σ p of the image of size 'p' is computed, - a reduction step in the course of which the resolution of the image of size 'p' is reduced by a factor of 2 into an image of size 'p+1', where the values X ij p + 1 of the pixels of the image of size 'p+1' are computed with respect to the normalized values x ij p of the pixels of the image of size 'p', - an incrementation step in the course of which the index 'p' is incremented by 1, - a test step in the course of which the value of the index 'p' is compared with N, - if the result of the test step is negative, a looping step in the course of which the method of detection loops back to the cutting step, - a second normalization step in the course of which the variance σ p of each reduced image of size 'p' where p varies from 1 to N-1 is divided by the variance σ 0 of the image of size '0', - a vectorization step, in the course of which a vector of characteristics is constructed on the basis of the set of variances thus normalized σ 1 / σ 0, σ 2 / σ 0,..., σ N -1 / σ 0, - a multiplication step in the course of which is computed the scalar product of the vector of characteristics and of the predetermined eigenvector arising from the LDA, - a comparison step in the course of which the scalar product thus computed is compared with a reference threshold value, and - a classification step in the course of which the image of size '0' is classed into the "cast shadows" class if the score is greater than the reference threshold value, and is not classed into the "cast shadows" class if the score is less than the reference threshold value.