Detecting whether a fingerprint is present in an image is of fundamental importance in capture devices and in the maintenance of existing fingerprint-based biometric systems. Biometric systems and fingerprint recognition systems have become very widespread in the recent years, both in mobile devices and through increased usage in border controls, electronic national identification systems, controlled work duration time and so on. Any biometric system is that the quality of the data that enters the system is of the highest possible quality to facilitate ease. But the system error rates are sensitive to the quality of the enrolled sample due to subsequent interactions with the biometric system, results in a comparison being made against the enrolled sample. If the enrolled sample is of poor quality then the comparisons are more likely to result in a false non-match. We present a fingerprint enhancement algorithm, which can adaptively improve the clarity of ridge and furrow structures of input fingerprint images based on the estimated local ridge orientation and frequency. In this research shows the Gaussian, Median, Mean, Minimum, Maximum and Variance filtering techniques, Canny, Robert, Prewitt, Log and Fuzzy edge detection methods, enhancement Sharpen techniques, Morphological thinning methods, combined their methods and some experiment results in fingerprint images. The enhancement, Sharpen techniques, Canny edge detection, Gaussian filter and morphological thinning methods combined gave the good result and much more reduced noise, increased edges, lightning and enhanced fingerprint image. The morphological thinning method gave the good result our experiments fingerprint images.