We have developed a new Linear Support Vector Machine (SVM) training algorithm called OCAS. Its computational effort scales linearly with the sample size. In an extensive empirical evaluation OCAS significantly outperforms current state of the art SVM solvers, like SVM light , SVM perf and BMRM, achieving speedups of over 1,000 on some datasets over SVM light and 20 over SVM perf , while obtaining the same precise Support Vector solution. OCAS even in the early optimization steps shows often faster convergence than the so far in this domain prevailing approximative methods SGD and Pegasos. Effectively parallelizing OCAS we were able to train on a dataset of size 15 million examples (itself about 32GB in size) in just 671 secondsa competing string kernel SVM required 97,484 seconds to train on 10 million examples subsampled from this dataset.

We propose a principled approach to supervised learning of facial landmarks detector based on the Deformable Part Models (DPM). We treat the task of landmarks detection as an instance of the structured output classification. To learn the parameters of the detector we use the Structured Output Support Vector Machines algorithm. The objective function of the learning algorithm is directly related to the performance of the detector and controlled by the userdefined loss function, in contrast to the previous works. Our proposed detector is real-time on a standard computer, simple to implement and easily modifiable for detection of various set of landmarks. We evaluate the performance of our detector on a challenging "Labeled Faces in the Wild" (LFW) database. The empirical results show that our detector consistently outperforms two public domain implementations based on the Active Appearance Models and the DPM. We are releasing open-source code implementing our proposed detector along with the manual annotation of seven facial landmarks for nearly all images in the LFW database.1 There also exists a successful commercial solution OKAO Vision Facial Feature Extraction API (http://www.omron.com) which is used for example in Picasa TM or Apple iPhoto TM software.

No abstract

No abstract

No abstract