Abstract
Recently, the classification of the head pose has gained incremented attention due to the rapid development of HCI/HRI interfaces. The resoluteness of head pose plays a considerable part in interpreting the person’s focus of attention in human-robot or human-human intercommunications since it provides explicit information of his/her attentional target. This paper proposes a geometrical feature-based human head pose classification using deep convolution networks. An MTCNN framework is implemented to identify the human face and a ResNet50 layered architecture built to classify nine head poses. The system is trained with 2, 85, 000 and tested by 1, 15, 500 head pose images. The proposed system achieved \(90.00\%\) precision for nine head pose classes.
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This work was supported by ICT Division, Ministry of Posts, Telecommunications and Information Technology, Bangladesh.
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Afroze, S., Hoque, M.M. (2021). Head Pose Classification Based on Deep Convolution Networks. In: Misra, R., Kesswani, N., Rajarajan, M., Bharadwaj, V., Patel, A. (eds) Internet of Things and Connected Technologies. ICIoTCT 2020. Advances in Intelligent Systems and Computing, vol 1382. Springer, Cham. https://doi.org/10.1007/978-3-030-76736-5_42
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