Precise head pose estimation on HPD5A database for attention recognition based on convolutional neural network in human-computer interaction

摘要

Head pose estimation (HPE) under infrared imaging has become more and more common in the human-computer interaction. In this paper, we proposed a novel HPE with convolutional neural network and established a precise head pose database under 5 degrees angle (HPD5A) for human attention recognition. Specially, the HPD5A database includes 729 infrared head pose images from different subjects with and without glasses, which corresponds to drivers who wear glasses or do not wear glasses. To verify the availability and usability of the HPD5A database, the benchmark evaluations are performed on our database using traditional standard HPE classification methods with and without principal component analysis. The methods include linear discriminant analysis, K-nearest neighbor, random forest and Naive Bayes classifiers. We also design and implement a convolutional neural network architecture as one of elementary assessments. All the results are provided for future reference. The developed deep learning technique could obtain the state-of-the-art performance on the HPD5A database. This database will certainly help in the development of model for infrared HPE and be beneficial to the attention recognition in human-computer interaction system.

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