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Usually, in touchless 3D Fingerprint recognition system the effective area is increased by taking multiple images of the finger. This is done to mosaic or stitch these images together. The key problem here is the need to effectively extract feature points from these images individually and match them. There has never been a complete survey on the different methods of image feature detection specific for fingerprints. The goal of this paper is to have a comparative study visualizing the merits and demerits of the methods. We evaluate the performance of existing image feature detection techniques, such as, Difference-of-Gaussian, Hessian, Hessian Laplace, Harris Laplace, Multiscale Hessian, Multiscale Harris and OpenSURF, on a database that contains multiple images of a finger. The process involves (i) feature detection, (ii) feature matching and validation, and (iii) image stitching. Also we evaluate the performance by visually examining the mosaicked images as well as the number of matches. Computer simulation are presented and the goal is to make a comparison of the existing feature detection algorithms.