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In this paper we propose a proprietary static hand pose database called OUHANDS and protocols for training and evaluating hand pose classification and hand detection methods. A comparison between the OUHANDS database and existing databases is given. Baseline results for both of the protocols are presented.

Data is in a very important position for pattern recognition tasks including eye gaze estimation. In the literature, most researchers used normal face datasets, which are not specifically designed for eye gaze estimation. As a result, it is difficult to obtain fine labeled eye gaze direction. Therefore large datasets with well-defined gaze directions are desired. To facilitate related researches, we collect and establish the Oulu Multi-pose Eye Gaze Dataset. Inspired by the psychological observation that gaze direction is intrinsically linked with the head orientation, we are devoted to a new data set of eye gaze images captured under multiple head poses. It finally results in a dataset containing over 40K images from 50 subjects, who were asked to fixate on 10 special points on screen under different poses respectively. We investigate a new eye gaze estimation approach by using the IGO based description, and compare it with other popular eye gaze estimation approaches to provide the baseline results on our dataset.

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Differences in illumination conditions cause significant challenges for any 2-D face recognition algorithm. One of the methods to counter these effects is image preprocessing before feature extraction. In this paper we present a new preprocessing approach that uses custom filters obtained through an optimization procedure striving for most suitable preprocessing filters for the selected feature extractor and distance measure. We experiment with it using Local Binary Pattern texture features and x2 histogram distance metric. Results are provided for Face Recognition Grand Challenge (FRGC) 1.0.4 dataset.