Guest editorial: special issue on human abnormal behavioural analysis
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Computer Vision and Pattern Recognition,Hardware and Architecture,Software
Link
http://link.springer.com/content/pdf/10.1007/s00138-019-01031-x.pdf
Reference16 articles.
1. Bablani, A., Edla, D.R., Tripathi, D., Kuppili, V.: An efficient concealed information test: Eeg feature extraction and ensemble classification for lie identification. Mach. Vis. Appl. (2018). https://doi.org/10.1007/s00138-018-0950-y
2. Bellantonio, M., Haque, M.A., Rodriguez, P., Nasrollahi, K., Telve, T., Escalera, S., Gonzalez, J., Moeslund, T.B., Rasti, P., Anbarjafari, G.: Spatio-temporal pain recognition in cnn-based super-resolved facial images. In: Video Analytics. Face and Facial Expression Recognition and Audience Measurement, pp. 151–162. Springer (2016)
3. Ding, Z., Li, W., Ogunbona, P., Qin, L.: A real-time webcam-based method for assessing upper-body postures. Mach. Vis. Appl. (2019). https://doi.org/10.1007/s00138-019-01033-9
4. Gorbova, J., Colovic, M., Marjanovic, M., Njegus, A., Anbarjafari, G.: Going deeper in hidden sadness recognition using spontaneous micro expressions database. Multimedia Tools Appl (2019). https://doi.org/10.1007/s11042-019-7658-5
5. Haque, M.A., Bautista, R.B., Noroozi, F., Kulkarni, K., Laursen, C.B., Irani, R., Bellantonio, M., Escalera, S., Anbarjafari, G., Nasrollahi, K., et al.: Deep multimodal pain recognition: a database and comparison of spatio-temporal visual modalities. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pp. 250–257. IEEE (2018)
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