Eye Localization Using Convolutional Neural Networks and Image Gradients
Eye detection is a preprocessing step in many methods using facial images. Some algorithms to detect eyes are based on the characteristics of the gradient flow in the iris-sclera boundary. These algorithms are usually applied to the whole face and a posterior heuristic is used to remove false positives. In this paper, we reverse that approach by using a Convolutional Neural Network (CNN) to solve a regression problem and give a coarse estimate of the eye regions, and only then do we apply the gradient-based algorithms. The CNN was combined with two gradient-based algorithms and the results were evaluated regarding their accuracy and processing time, showing the applicability of both methods for eye localization.
Abdulameer, M., sheikh abdullah, S., and Othman, Z. (2014). A modified active appearance model based on an adaptive artificial bee colony. 2014:879031.
BioID (2017). The BioID Face Database. https://www.bioid.com/facedb/.
Dutta, A., Günther, M., Shafey, L. E., Marcel, S., Veldhuis, R., and Spreeuwers, L. (2015). Impact of eye detection error on face recognition performance. IET Biometrics, 4(3):137–150.
Farkas, L. G., Hreczko, T., Kolar, J. C., and Munro, I. R. (1985). Vertical and horizontal proportions of the face in young adult North American Caucasians: revision of neoclassical canons. Plastic and Reconstructive Surgery, 75(3):328–337.
Hume, T. (2012). Simple, accurate eye center tracking in OpenCV. http://thume.ca/ projects/2012/11/04/simple-accurate-eye-center-tracking-in-opencv/.
Jesorsky, O., Kirchberg, K. J., and Frischholz, R. (2001). Robust face detection using the Hausdorff distance. In Bigün, J. and Smeraldi, F., editors, Audio- and Video-Based Biometric Person Authentication, Third International Conference, AVBPA 2001 Halmstad,
Sweden, June 6-8, 2001, Proceedings, volume 2091 of Lecture Notes in Computer Science, pages 90–95. Springer.
Kar, A. and Corcoran, P. (2017). A review and analysis of eye-gaze estimation systems, algorithms and performance evaluation methods in consumer platforms. IEEE Access, 5:16495–16519.
Kothari, R. and Mitchell, J. L. (1996). Detection of eye locations in unconstrained visual images. In Proceedings 1996 International Conference on Image Processing, Lausanne, Switzerland, September 16-19, 1996, pages 519–522. IEEE Computer Society.
Mushfieldt, D., Ghaziasgar, M., and Connan, J. (2013). Robust facial expression recognition in the presence of rotation and partial occlusion. In Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference, SAICSIT ’13, pages 186–193, New York, NY, USA. ACM.
Nouri, D. (2014). Using convolutional neural nets to detect facial keypoints tutorial. http://danielnouri.org/notes/2014/12/17/using-convolutional-neural-nets-to-detect-nnfacial-keypoints-tutorial/.
Ozhiganov, I. (2016). Convolutional Neural Networks for Object Detection. http://rnd.azoft.com/convolutional-neural-networks-object-detection/.
Patterson, J. and Gibson, A. (2017). Deep Learning: A Practitioner’s Approach. O’Reilly, Sebastopol, CA.
Timm, F. and Barth, E. (2011). Accurate eye centre localisation by means of gradients. In Mestetskiy, L. and Braz, J., editors, VISAPP 2011 - Proceedings of the Sixth International Conference on Computer Vision Theory and Applications, Vilamoura, Algarve, Portugal, 5-7 March, 2011, pages 125–130. SciTePress.
Viola, P. and Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. pages 511–518.