Abstract
We propose a method for the coarse classification of head pose from low-resolution images. We devise a mechanism that uses a cascade of three binary Support Vector Machines (SVM) classifiers. We use two sets of appearance features, Similarity Distance Map (SDM) and Gabor Wavelet (GW) as input to the SVM classifiers. For training, we employ a large dataset that combines five publicly available databases. We test our approach with cross-validation using the eight databases and on videos we collected in a lab experiment. We found a significant improvement in the results achieved by the proposed method over existing schemes. In the cross-validation test, we achieved a head pose detection accuracy of 98.60%. Moreover, we obtained a head pose detection accuracy of 93.76% for high-resolution and 89.81% for low-resolution videos collected in the lab under loosely constrained conditions.
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Khaki, M., Ayoub, I., Javadtalab, A., Osman, H.A. (2019). Robust Classification of Head Pose from Low Resolution Images. In: Vento, M., Percannella, G. (eds) Computer Analysis of Images and Patterns. CAIP 2019. Lecture Notes in Computer Science(), vol 11678. Springer, Cham. https://doi.org/10.1007/978-3-030-29888-3_43
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