1. A method for real time gender estimation using only fingerprint information comprising: obtaining fingerprints which include an equal number of fingerprints from men and women of different ages and ethnicities taken from an Automated Fingerprint Recognition System (AFIS), establishing an appropriate fingerprint database of the obtained fingerprints, determining whether a fingerprint is from a right hand or a left hand, cropping an area of 4×4 mm, 5×5 mm, 6×6 mm, 7×7 mm, or 8×8 mm starting from a core point of the fingerprint to an upper-left part of the fingerprint if the fingerprint is from a right hand, cropping an area of 4×4 mm, 5×5 mm, 6×6 mm, 7×7 mm, or 8×8 mm starting from the core point of the fingerprint to an upper-right part of the fingerprint if the fingerprint is from a left hand, applying improvement and noise reduction procedures on the cropped area of the fingerprint, plotting a diagonal line starting from the core point to an upper-left corner if the fingerprint is from a right hand, plotting a diagonal line from the core point to the upper-right corner if the fingerprint is from a left hand, preparing a bit string line array by taking pixels on the diagonal line as black and white, determining the number of independent black clusters in the line array for obtaining a ridge count, determining a black character length by calculating the number of independent black pixels for all ridges for obtaining a ridge thickness, obtaining average ridge thickness and ridge count from the fingerprints of men and women, determining input and output parameters for an intelligent system by considering obtained data input parameters such as including an orthogonal pixel value of a fingerprint, the ridge count, the ridge thickness, and the average ridge thickness and ridge count for fingerprints of men and women, distinguishing fingerprints from men and women by using numeric output values, creating an intelligent system model based on artificial neural networks (ANN) by selecting data number, determining neurons for input and output layers, a number of hidden layers and the neurons in the hidden layers, and functions preferred in the neurons, using back propagation, Levenberg-Marquardt, genetic algorithm, or fuzzy logic for training the created model, training the created ANN model with a chosen learning algorithm by a selected number of samples until getting targeted error rate or getting targeted performance, testing the obtained intelligent model with a chosen number of fingerprints, and determining a threshold value that separates men and women fingerprints by obtained values.