浏览全部资源
扫码关注微信
收稿：2018-07-04，
修回：2018-12-29，
纸质出版：2019-06-16
移动端阅览
目的
2
视线追踪是人机交互的辅助系统，针对传统的虹膜定位方法误判率高且耗时较长的问题，本文提出了一种基于人眼几何特征的视线追踪方法，以提高在2维环境下视线追踪的准确率。
方法
2
首先通过人脸定位算法定位人脸位置，使用人脸特征点检测的特征点定位眼角点位置，通过眼角点计算出人眼的位置。直接使用虹膜中心定位算法的耗时较长，为了使虹膜中心定位的速度加快，先利用虹膜图片建立虹膜模板，然后利用虹膜模板检测出虹膜区域的位置，通过虹膜中心精定位算法定位虹膜中心的位置，最后提取出眼角点、虹膜中心点等信息，对点中包含的角度信息、距离信息进行提取，组合成眼动向量特征。使用神经网络模型进行分类，建立注视点映射关系，实现视线的追踪。通过图像的预处理对图像进行增强，之后提取到了相对的虹膜中心。提取到需要的特征点，建立相对稳定的几何特征代表眼动特征。
结果
2
在普通的实验光照环境中，头部姿态固定的情况下，识别率最高达到98.9%，平均识别率达到95.74%。而当头部姿态在限制区域内发生变化时，仍能保持较高的识别率，平均识别率达到了90%以上。通过实验分析发现，在头部变化的限制区域内，本文方法具有良好的鲁棒性。
结论
2
本文提出使用模板匹配与虹膜精定位相结合的方法来快速定位虹膜中心，利用神经网络来对视线落点进行映射，计算视线落点区域，实验证明本文方法具有较高的精度。
Objective
2
Eye gaze is an input mode that has a potential to serve as an efficient computer interface. Eye movement has consistently been a research hotspot. Knowledge on gaze tracking can provide valuable information on a person's point of attention. The methods used at present are mainly model and regression based. The model-based method extracts facial features and calculates the 3D gaze direction through the geometric relationship between facial features. However
to obtain good accuracy
this method requires individual calibration
which is difficult and reduces user experience. Meanwhile
the regression-based method utilizes powerful computer learning technology to perform mapping from eye appearance characteristics to the gaze direction. Compared with the model-based method
the regression-based method avoids the modeling of the complicated eyeball structure and only needs to collect a large amount of data. Regression-based approaches can be further divided into feature-and appearance-based methods. The feature-based regression method learns the mapping function from an eye feature to the gaze direction
whereas appearance-based regression learns the mapping function of gaze direction from eye appearance. Learning algorithms use traditional support vector regression
random forest
and the latest in-depth learning technology. However
this method requires one or more data sets
thus making the model complicated. Meanwhile
regression-based methods commonly use additional data to compensate for head movements. In addition
substantial data are needed to learn a good mapping function. To improve line-of-sight tracking accuracy in a 2D environment
a new method based on the geometric features of the human eyes is proposed to solve the problem of high error rate and large time consumption of traditional iris location methods.
Method
2
First
the position of the face is located by a face location algorithm. The location of the eye angle point is determined by the feature point of the feature point detection
and the eye area is calculated by the angle point. A traditional iris location method may take a long time to locate the iris center. To increase the speed of iris center location
an iris template is established by an iris image and used to detect the location of the iris region. Subsequently
the iris center position is roughly located. Second
the iris center position is located by an iris center precise location algorithm. Through facial feature point localization and iris center localization
the corners of the eye and the iris center are obtained and used as basic information to describe eye movement vectors. The extracted eye motion vector comprises only the information on eye corners and iris center points; thus
the angle is introduced based on the position relation of the points
and the distance from the departure information is adopted as the final eye motion vector. In this study
the neural network model is used to judge the point of sight
and the eye movement vector is utilized as the input feature of the neural network model. Then
the mapping relation of the gaze point is established to realize line-of-sight tracking.
Result
2
A camera is used to record videos as the neural network training dataset. In the feature extraction stage
the original data are preprocessed to enhance image quality
thus making the iris center extraction accurate. Training results are obtained via feature extraction
training
and testing. Results show that in an ordinary experimental light environment
the recognition rate reaches 98.9% when the head pose is fixed
and the average recognition rate reaches 95.74%. When the head posture changes
the recognition rate of the algorithm changes to some extent
but the recognition rate can remain stable if stable eye movement features are extracted. When the restricted area of head posture changes
the recognition rate is still high
and the average recognition rate exceeds 90%. Experimental results show that the proposed method has good robustness to restricted area of head variation.
Conclusion
2
In this study
a neural network is used to map eye images and the gaze point. Hence
the system does not need to use multiple cameras and infrared light sources
nor does it need camera calibration. A single camera system with no light source is utilized to locate the iris center through a combination of template matching and iris precision positioning. Compared with other methods
this system has a simpler structure
which is realized by using only a single webcam without an auxiliary light source and camera calibration. The neural network is adopted to map the line-of-sight landing point and calculate the line-of-sight landing area. Relatively stable features are extracted in an ordinary light source environment. Experiments show that the method performs well when the camera detects a complete head image in a certain range of head posture changes.
Beymer D, Flickner M. Eye gaze tracking using an active stereo head[C]//Proceedings of 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. Madison, WI, USA: IEEE, 2003: Ⅱ-451.[ Doi: 10.1109/CVPR.2003.1211502 http://dx.doi.org/10.1109/CVPR.2003.1211502 ]
Wang K, Ji Q. Real time eye gaze tracking with Kinect[C]//Proceedings of the 23rd International Conference on Pattern Recognition. Cancun, Mexico: IEEE, 2016: 2752-2757.[ Doi: 10.1109/ICPR.2016.7900052 http://dx.doi.org/10.1109/ICPR.2016.7900052 ]
Wang K, Ji Q. Real time eye gaze tracking with 3D deformable eye-face model[C]//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, I taly: IEEE, 2017: 1003-1011.[ Doi: 10.1109/ICCV.2017.114 http://dx.doi.org/10.1109/ICCV.2017.114 ]
Zhu Z W, Ji Q, Bennett K P. Nonlinear eye gaze mapping function estimation via support vector regression[C]//Proceedings of the 18th International Conference on Pattern Recognition. Hong Kong, China: IEEE, 2006: 1132-1135.[ Doi: 10.1109/ICPR.2006.864 http://dx.doi.org/10.1109/ICPR.2006.864 ]
Morimoto C H, Mimica M R M. Eye gaze tracking techniques for interactive applications[J]. Computer Vision and Image Understanding, 2005, 98(1):4-24.[DOI:10.1016/j.cviu.2004.07.010]
Cheng H, Liu Y Q, Fu W H, et al. Gazing point dependent eye gaze estimation[J]. Pattern Recognition, 2017, 71:36-44.[DOI:10.1016/j.patcog.2017.04.026]
Lu F, Chen X W, Sato Y. Appearance-based gaze estimation via uncalibrated gaze pattern recovery[J]. IEEE Transactions on Image Processing, 2017, 26(4):1543-1553.[DOI:10.1109/TIP.2017.2657880]
Zhang X X, Sugano Y, Fritz M, et al. Appearance-based gaze estimation in the wild[C]//Proceedings of 2015IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA: IEEE, 2015: 4511-4520.[ Doi: 10.1109/CVPR.2015.7299081 http://dx.doi.org/10.1109/CVPR.2015.7299081 ]
Maio W, Chen J X, Ji Q. Constraint-based gaze estimation without active calibration[C]//Proceedings of the Face and Gesture 2011. Santa Barbara, CA, USA: IEEE, 2011: 627-631.[ Doi: 10.1109/FG.2011.5771469 http://dx.doi.org/10.1109/FG.2011.5771469 ]
Wood E, Baltruaitis T, Zhang X C, et al. Rendering of eyes for eye-shape registration and gaze estimation[C]//Proceedings of the IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015: 3756-3764.[ Doi: 10.1109/ICCV.2015.428 http://dx.doi.org/10.1109/ICCV.2015.428 ]
Wang Y L, Ma J, Li H Q, et al. Race classification based on deep features and Fisher vectors of iris texture[J]. Journal of Image and Graphics, 2018, 23(1):28-38.
王雅丽, 马静, 李海青, 等.基于虹膜纹理深度特征和Fisher向量的人种分类[J].中国图象图形学报, 2018, 23(1):28-38.[DOI:10.11834/jig.170219]
Huang S C, Fang X Y, Zhou J, et al. Image local blur measure based on BP neural network[J]. Journal of Image and Graphics, 2015, 20(1):20-28.
黄善春, 方贤勇, 周健, 等.基于BP神经网络的图像局部模糊测量[J].中国图象图形学报, 2015, 20(1):20-28.[DOI:10.11834/jig.20150103]
Xiong X H, De La Torre F. Supervised descent method and its applications to face alignment[C]//Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, OR, USA: IEEE, 2013: 532-539.[ Doi: 10.1109/CVPR.2013.75 http://dx.doi.org/10.1109/CVPR.2013.75 ]
Viola P, Jones M. Rapid object detection using a boosted cascade of simple features[C]//Proceedings of 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Kauai, HI, USA: IEEE, 2001: 905-910.[ Doi: 10.1109/CVPR.2001.990517 http://dx.doi.org/10.1109/CVPR.2001.990517 ]
Lisu L. Research on face detection classifier using an improved adboost algorithm[C]//Proceedings of 2008 International Symposium on Computer Science and Computational Technology. Shanghai: IEEE, 2008: 78-81.[ Doi: 10.1109/ISCSCT.2008.352 http://dx.doi.org/10.1109/ISCSCT.2008.352 ]
Jesorsky O, Kirchberg K J, Frischholz R. Robust face detection using the Hausdorff distance[C]//International Conference on Audio- & Video-based Biometric Person Authentication. 2001.[ Doi: 10.1007/3-540-45344-X_14 http://dx.doi.org/10.1007/3-540-45344-X_14 ]
Daugman J G. High confidence visual recognition of persons by a test of statistical independence[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993, 15(11):1148-1161.[DOI:10.1109/34.244676]
Daugman J. How iris recognition works[C]//Proceedings of 2002 International Conference on Image Processing. Rochester, NY, USA: IEEE, 2002: 715-739.[ Doi: 10.1109/ICIP.2002.1037952 http://dx.doi.org/10.1109/ICIP.2002.1037952 ]
Shamsi M, Saad P B, Ibrahim S B, et al. Fast algorithm for iris localization using Daugman circular integro differential operator[C]//Proceedings of 2009 International Conference of Soft Computing and Pattern Recognition. Malacca, Malaysia: IEEE, 2009: 393-398.[ Doi: 10.1109/SoCPaR.2009.83 http://dx.doi.org/10.1109/SoCPaR.2009.83 ]
Zhang X D, Dong X P, Wang Q, et al. Location algorithm of RGB iris image based on integro-differential operators[J]. Journal of Northeastern University:Natural Science, 2011, 32(11):1550-1553.
张祥德, 董晓鹏, 王琪, 等.基于微积分算子的彩色虹膜图像定位算法[J].东北大学学报:自然科学版, 2011, 32(11):1550-1553.
Sesma-Sanchez L, Zhang Y X, Bulling A, et al. Gaussian processes as an alternative to polynomial gaze estimation functions[C]//Proceedings of the 9th Biennial ACM Symposium on Eye Tracking Research & Applications. Charleston, South Carolina: ACM, 2016: 229-232.[ Doi: 10.1145/2857491.2857509 http://dx.doi.org/10.1145/2857491.2857509 ]
Klare B, Jain A K. Heterogeneous face recognition: matching NIR to visible light images[C]//Proceedings of the 20th International Conference on Pattern Recognition. Istanbul, Turkey: IEEE, 2010: 1513-1516.[ Doi: 10.1109/ICPR.2010.374 http://dx.doi.org/10.1109/ICPR.2010.374 ]
Zhang C, Chi J N, Zhang Z H, et al. A novel eye gaze tracking technique based on pupil center cornea reflection technique[J]. Chinese Journal of Computers, 2010, 33(7):1272-1282.
张闯, 迟健男, 张朝晖, 等.一种新的基于瞳孔-角膜反射技术的视线追踪方法[J].计算机学报, 2010, 33(7):1272-1285.[DOI:10.3724/SP.J.1016.2010.01272]
Zhu Z W, Ji Q. Novel eye gaze tracking techniques under natural head movement[J]. IEEE Transactions on Biomedical Engineering, 2007, 54(12):2246-2260.[DOI:10.1109/TBME.2007.895750]
相关作者
相关机构
京公网安备11010802024621
