Face Fudicial Detection project in Hibernate framework.

Its a project on detection of face using real time . great project.Facial fiducial detection is a challenging problem for several reasons like varying pose, appearance, expression, partial occlusion and others. In the past, several ap- proaches like mixture of trees [32], regression based meth- ods [8], exemplar based methods [7] have been proposed to tackle this challenge. In this paper, we propose an exemplar based approach to select the best solution from among outputs of regression and mixture of trees based algorithms (which we call can- didate algorithms). We show that by using a very simple SIFT and HOG based descriptor, it is possible to identify the most accurate fiducial outputs from a set of results pro- ducedbycandidatealgorithmsonanygiventestimage. Our approach manifests as two algorithms, one based on opti- mizing an objective function with quadratic terms and the other based on simple kNN. Both algorithms take as input fiducial locations produced by running state-of-the-art can- didate algorithms on an input image, and output accurate fiducials using a set of automatically selected exemplar im- ages with annotations. Our surprising result is that in this case, a simple algorithm like kNN is able to take advantage of the seemingly huge complementarity of these candidate algorithms, better than optimization based algorithms. We do extensive experiments on several datasets, and show that our approach outperforms state-of-the-art con- sistently. In some cases, we report as much as a 10% im- provement in accuracy. We also extensively analyze each component of our approach, to illustrate its efficacy. An implementation and extended tech- nical report of our approach is available www.sites.google.com/site/wacv2016facefiducialexemplars

Details not available

Details not available