In this paper we introduce novel face representations, the masked Trace transform (MTT), the shape Trace transform (STT) and the weighted Trace transform (WTT), for recognising faces in a face authentication system. We first transform the image space to the Trace transform space to produce the MTT. We then identify the points of the MTT which take similar values irrespective of intraclass variations and this way we create the WTT. Next we threshold the MTT and extract the edges of the thresholded regions to produce some shapes that characterise the person. This is the STT. Therefore, each person in the database is represented by their WTT and STT. We estimate the dissimilarity between two shapes by a new measure we propose, the Hausdorff context. Reinforcement learning is used to search for the optimal parameter values of the algorithm. Shape and features from the MTT are then integrated at the decision level, by a classifier combination algorithm. Our system is evaluated with experiments on the XM2VTS database using 2360 face images. We achieve a Total Error Rate (TER) of 0.18%, which is the lowest error among all other reported methods which used the same data and the same evaluation protocol in a recently published study.
['Sanun Srisuk', 'Maria Petrou', 'Werasak Kurutach', 'Alexander Kadyrov']
A face authentication system using the trace transform