Given an annotated dataset, the feature detectors can be learned independently from each other. The learning objective of a discriminative patch model is to construct an image patch that, when cross-correlated with an image region containing the facial feature, yields a strong response at the fease. Mathematically, this can be expressed as:
Here, P denotes the patch model, I denotes the ith training image, I(a:b, c:d) denotes the rectangular region whose top-left and bottom-right corners are located at (a, c) and (b, d), respectively. The period symbol denotes the inner product operation and R denotes the ...
