Natural face images are both content and context-rich, in the sense that they carry significant immersive information via depth cues embedded in the form of self-shadows or a space varying blur. Images of planar face prints, on the other hand, tend to have lower contrast and also suppressed depth cues. In this work, a solution is proposed, to detect planar print spoofing by enhancing self-shadow patterns present in face images. This process is facilitated and siphoned via the application of a non-linear iterative functional map, which is used to produce a contrast reductionist image sequence, termed as an image life trail. Subsequent images in this trail tend to have lower contrast in relation to the previous iteration. Differences taken across this image sequence help in bringing out the self-shadows already present in the original image. On a client specific mode, when subjects and faces are registered, secondary life trail differential statistics which capture the prominence of self-shadow information, indicate that planar print-images tend to have highly suppressed self-shadows when compared with natural face images. A simple statistical model leading to an an elaborate tuning procedure, based on a reduced set of training images was developed to first identify the optimal parameter set corresponding to a specific dataset and then adapt the feature-vectors so that the error-rates were minimized. Overall mean error rate for the calibration-set (reduced CASIA dataset) was found to be 0.3106% and the error rates for other datasets such OULU-NPU and CASIA-SURF were 1.1928% and 2.2462% respectively.