1. A method for real time gender estimation with taking only fingerprint information from a person and examining it comprising, making provision for obtaining fingerprint samples which include equal number of men and women fingerprints and include different ages from any number of different people, and fingerprints which will be working on is taken with an Automated Fingerprint Recognition System (AFIS), establishing an appropriate fingerprint database with using mentioned fingerprints, determining the fingerprint side (left or right side) with the use of a develop software, if the captured fingerprint is from the upper-side, an area as 4×4 mm, 5×5 mm, 6×6 mm, 7×7 mm, 8×8 mm is cropped from the upper-left part of the fingerprint image of the core point, if the left fingerprint is captured, the upper-right part of an image based on the core point is taken, applying improvement and noise reduction procedures on fingerprint parts, taking a sample of part (6×6 mm) which is ready to use after handling, plotting a diagonal line from core point to upper-left corner of sample, preparing a bit string line array with taking pixels which are on this diagonal as black (1) and white (0), determining the number of independent is dusters in line array, determining independent “black” character number (1s clusters) in line array and assigning this sum as ridge count, determining “black” character length with considering the number of independent 1s and finding this value for all ridges and obtaining ridge thickness from that process, finding is and 0s thickness, and ridge counts, obtaining average ridge value of men and women with the procedures which are specified above, Determining the input and output parameters for intelligent system with considering obtained data input parameters as, orthogonal pixel value of taken part of fingerprint, obtained ridge count, ridge thickness, the number of average ridge values of men and women which is determined before, Distinguishing men and women with the numeric output values “1” and “−1” or “1” and “0”, Envisioning the intelligent system model (ANN structure) to be created by selected data number, determining the neurons for input and output layers, the number of hidden layers and the neurons in the hidden layers, the functions preferred in the neurons using trial and error approach, genetic algorithm or anova, Using an appropriate existing learning algorithm in literature (back propagation, Levenberg-Marquardt, genetic algorithm, fuzzy logic etc.) for training the created model, Training the created ANN model with a chosen learning algorithm by the selected number of samples until getting targeted error rate or getting targeted performance (0.01 RMS error rate), After training, testing the obtained intelligent model with a chosen number of fingerprint data (test result cannot be larger than 0.05 RMS error), Determining a threshold level that separates man and woman by obtained values and determining this as a man if this is between 0 and 1, as a woman if this is between 0 and −1.