Marked point process model for facial wrinkle detection

We propose a new model for wrinkle detection in human faces using a marked point process. In order to detect an arbitrary shape of wrinkles, we represent them as a set of line segments, where each segment is characterized by its length and orientation. We propose a probability density of wrinkle model which exploits local edge profile and geometric properties of wrinkles. To optimize the probability density of wrinkle model, we employ reversible jump Markov chain Monte Carlo sampler with delayed rejection. Experimental results demonstrate that the new algorithm detects facial wrinkles more accurately than a recent state-of-the-art method.