1. A method for fingerprint classification, comprising the steps of: scanning a fingerprint to generate a fingerprint image, FP, having a horizontal resolution of M pixels and a vertical resolution of N pixels; generating a set of responses for each (i,j)-th pixel of the fingerprint image FP, G k (i,j), where 1≦i≦M and 1≦j≦N, by convolving the fingerprint image FP with a set of filters, G θ k , for k=1,. .. , 8, such that G k (i,j)=FP*G θ k over k=1,. .. , 8, wherein G θ k =G x cos θ k +G y sin θ k , where θ k = ( k - 1 ) ⁢ π 8 for k=1,. .. , 8, and G x and G y are, respectively, horizontal and vertical Gaussian derivative filters respectively given by G x = - x 2 ⁢ πσ 4 ⁢ ⅇ - x 2 + y 2 2 ⁢ σ 2 ⁢ ⁢ and ⁢ ⁢ G y = - y 2 ⁢ πσ 4 ⁢ ⅇ - x 2 + y 2 2 ⁢ σ 2 , where σ is a parameter representative of width of a Gaussian function; thresholding each response G k (i,j) such that, for each (i,j)-th pixel, if a value of the respective response G k (i,j) is greater than zero, then a corresponding thresholded value, s k , is equal to one, and if the value of the respective response G k (i,j) is less than or equal to zero, then the corresponding thresholded value, s k , is equal to zero; generating a local gradient directional binary pattern code by forming a binary number from the values of s k over k=1,. .. , 8 as s 1 s 2 s 3 s 4 s 5 s 6 s 7 s 8 ; generating a local gradient directional binary pattern code for each (i,j)-th pixel; computing a distribution of the local gradient directional binary pattern codes for all said pixels in the fingerprint image FP as a histogram and forms descriptor; and using the histogram and forms descriptor and a neural network to reduce substantially a search space to a predefined number of classes of fingerprints to be searched in order to identify the scanned fingerprint.