Dantcheva, A., Velardo, C., D'angelo, A., & Dugelay, J. L. (2011). Bag of soft biometrics for person identification. Multimedia Tools and Applications, 51(2), 739-777.
Jain, A. K., Dass, S. C., & Nandakumar, K. (2004). Soft biometric traits for personal recognition systems. In Biometric authentication (pp. 731-738). Springer, Berlin, Heidelberg.
Amos, B., Ludwiczuk, B., & Satyanarayanan, M. (2016). Openface: A general-purpose face recognition library with mobile applications. CMU School of Computer Science, 6.
Jaha, E. S., & Nixon, M. S. (2014, September). Soft biometrics for subject identification using clothing attributes. In IEEE International Joint Conference on Biometrics (pp. 1-6). IEEE.
Reid, D. A., Samangooei, S., Chen, C., Nixon, M. S., & Ross, A. (2013). Soft biometrics for surveillance: an overview. In Handbook of statistics (Vol. 31, pp. 327-352). Elsevier.
Zelinsky, Gregory J. “Understanding scene understanding” Frontiers in psychology vol. 4 954. 19 Dec. 2013, doi:10.3389/fpsyg.2013.00954
Busjahn, T., Schulte, C., Sharif, B., Begel, A., Hansen, M., Bednarik, R.,... & Antropova, M. (2014, July). Eye tracking in computing education. In Proceedings of the tenth annual conference on International computing education research (pp. 3-10). ACM.
Kasprowski, P., Komogortsev, O. V., & Karpov, A. (2012, September). First eye movement verification and identification competition at BTAS 2012. In 2012 IEEE fifth international conference on biometrics: theory, applications and systems (BTAS) (pp. 195-202). IEEE.
Cantoni, V., Porta, M., Galdi, C., Nappi, M., & Wechsler, H. (2014, November). Gender and age categorization using gaze analysis. In 2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems (pp. 574-579). IEEE.
Galdi, C., Wechsler, H., Cantoni, V., Porta, M., & Nappi, M. (2016). Towards demographic categorization using gaze analysis. Pattern Recognition Letters, 82, 226-231.
Cantoni, V., Galdi, C., Nappi, M., Porta, M., & Riccio, D. (2015). GANT: Gaze analysis technique for human identification. Pattern Recognition, 48(4), 1027-1038.
Rigas, I., Komogortsev, O., & Shadmehr, R. (2016). Biometric recognition via eye movements: Saccadic vigor and acceleration cues. ACM Transactions on Applied Perception (TAP), 13(2), 6.
Cazzato, D., Evangelista, A., Leo, M., Carcagnì, P., & Distante, C. (2016). A low-cost and calibration-free gaze estimator for soft biometrics: An explorative study. Pattern Recognition Letters, 82, 196-206.
Cazzato, D., Leo, M., Evangelista, A., & Distante, C. (2015, October). Soft Biometrics by Modeling Temporal Series of Gaze Cues Extracted in the Wild. In International Conference on Advanced Concepts for Intelligent Vision Systems (pp. 391-402). Springer, Cham.
https://developer.microsoft.com/en-us/windows/kinect [Last accessed: 17 June 2019]
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778
Baltrušaitis, T., Robinson, P., & Morency, L. P. (2016, March). Openface: an open source facial behavior analysis toolkit. In 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 1-10). IEEE.
Baltrusaitis, T., Robinson, P., & Morency, L. P. (2013). Constrained local neural fields for robust facial landmark detection in the wild. In Proceedings of the IEEE International Conference on Computer Vision Workshops (pp. 354-361).
Savitzky, A.; Golay, M.J.E. (1964). "Smoothing and Differentiation of Data by Simplified Least Squares Procedures". Analytical Chemistry. 36 (8): 1627-39. doi:10.1021/ac60214a047
M. Hein and T. Buhler. An Inverse Power Method for Nonlinear Eigenproblems with Applications in 1-Spectral Clustering and Sparse PCA. Advances in Neural Information Processing Systems 23 (NIPS 2010). Extended version available at http://arxiv.org/abs/1012.0774.
T. Buhler. A flexible framework for solving constrained ratio problems in machine learning. Ph.D. Thesis, Saarland Univ.,2015.http://scidok.sulb.unisaarland.de/volltexte/2015/6159/.
Huang, Q., Veeraraghavan, A. & Sabharwal, A. (2017) TabletGaze: dataset and analysis for unconstrained appearance-based gaze estimation in mobile tablets. Machine Vision and Applications (2017) 28: 445. https://doi.org/10.1007/s00138-017-0852-4
Leo, M., Furnari, A., Medioni, G. G., Trivedi, M., & Farinella, G. M. (2018). Deep Learning for Assistive Computer Vision. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 0-0).
