We consider the problem of locating instances of a known object in a novel scene by matching the fiducial features of the object. Our approach to the problem consists of two parts: a model for the appearance of the features and a model for the shape of the object. We then bind these parts together in a Bayesian framework and match the features sequentially, using the information about the locat...

We describe a new method for 2D fiducial tracking. We use region adjacency information together with angles between regions to encode IDs inside fiducials, whereas previous research by Kaltenbrunner and Bencina utilize region adjacency tree. Our method supports a wide ID range and is fast enough to accommodate real-time video. It is also very robust against false positive detection.

Humans focus attention on different face regions when recognizing face attributes. Most existing face attribute classification methods use the whole image as input. Moreover, some of these methods rely on fiducial landmarks to provide defined face parts. In this paper, we propose a cascade network that simultaneously learns to localize face regions specific to attributes and performs attribute ...