Handwritten digit recognition with a back-propagation network
Advances in neural information processing systems 2
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In applications where face orientation is necessary, but in unpretending environments in terms of lighting, equipment, resolution, etc, employing local tracking techniques would usually fail to give accurate results, regarding the issue of head pose estimation. However, in a similar manner, holistic techniques require the face to be well aligned with the training data. This pre-assumes correct and accurate face tracking, which is also a challenging issue. Here, we propose a face tracker, adjusted to each person's face chrominance values, and learnt online. Based on the face bounding box, Convolutional Neural Networks (CNNs) are employed, in order to calculate face orientation. CNNs are ideal for cases where a lot of distortions exist in the data, and the proposed architecture only utilizes subsets of classifiers, excluding those corresponding to rotation angles far from the current.