One of the oldest biometrics that has been used to uniquely identify a person is their fingerprint. Recent developments in research on fingerprint collection have made it possible to collect fingerprint data from a stand-off digital image. Each of the techniques developed so far have relied on either a very controlled capture environment to ensure only a single fingertip is collected or manual cropping of the image down to the fingertip. The main body of the research focuses on extracting the fingerprint itself. If fingerprint collection via digital image is ever to be fielded in the real world on such devices as smart phones or tablets it will be necessary for the software to automatically detect a single or multiple fingertips in an image and isolate them for extracting the fingerprint. We introduce an automatic fingertip detection algorithm that couples image processing techniques with a machine learning capability to successfully identify varying numbers of fingertips in digital images. Our algorithm proves that while it is difficult to remove all constraints from the capture environment it is achievable with the method we have developed and we can achieve a recall of 69.77% at a precision of 78.95%. This gives us the important capability to detect varying numbers of fingertips in an image and provide a crucial piece in what could be a complete automated fingerprint recognition system.