The week's pick
Random Articles
Reseach Article
Measurement of Unique Pupillary Distance using Modified Circle Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 180 - Number 9 |
| Year of Publication: 2018 |
| Authors: A. F. M. Saifuddin Saif, Md. Shahadat Hossain, Khandaker Tabin Hasan, Mashiour Rahman |
10.5120/ijca2018916125
|
A. F. M. Saifuddin Saif, Md. Shahadat Hossain, Khandaker Tabin Hasan, Mashiour Rahman . Measurement of Unique Pupillary Distance using Modified Circle Algorithm. International Journal of Computer Applications. 180, 9 ( Jan 2018), 1-5. DOI=10.5120/ijca2018916125
Abstract
This research investigated several equations and calculated the pupillary distance using pupils center coordinate. The prior research has provided two conceptual frameworks which may give a unique distance to all human. As Pupillary distance provides a core and unique information as like bio-metric for every human. The researcher did not explore the equation or pattern yet. This research proposed a framework called as Pixellary Pupil Distance and analyze the result. This research use pixel to calculate the pupillary distance where subtracts left pupil pixel with the right pupil and sum up with its distance pixel. Finally, the research sum all the pixel subtraction and its distance what this research called pupillary distance. This research builds and describes a custom dataset for test purpose because the pupil is the core element of eyes and this research did not find any precise dataset of it. The result shows the unicity of pupillary distance. This research also shows the computational time, false alarming rate, noise reduction which is described the performance of the purposed framework.
References
- A. S. Saif, M. S. Hossain, and K. T. Hasan, “A descriptive conceptual framework to measure distance of pupil using pixel value,” 6th International Conference on Informatics, Electronics , Vision, 2017.
- M. Popescu and R. T. Ionescu, Knowledge Transfer between Computer Vision and Text Mining::Similarity-based Learning Approaches. Springer, 2016.
- R. Bhatia, “Biometrics and face recognition techniques,” International Journals of Advanced Research in Computer Science and Software Engineering (IJARCSSE), vol. 3, no. 2, 2013.
- S. Ganga, “Chapter 4- methods for measuring distance in images.”
- V. Roselin.E.Chirchi, Dr.L.M.Waghmare, and E.R.Chirchi, “Iris biometric recognition for person identification in security systems,” International Journal of Computer Applications, pp. 0975 – 8887, 2011.
- G. Zhang, J. Chen, G. Su, and J. Liu, “Double-pupil location of face images,” Pattern Recognition, vol. 46, pp. 642–648, 2013.
- K. W. Choe, R. Blake, and S.-H. Lee, “Pupil size dynamics during fixation impact the accuracy and precision of videobased gaze estimation,” Vision Research, 2015.
- D. Tian, G. He, J.Wu, H. Chen, and Y. Jiang, “An accurate eye pupil localization approach based on adaptive gradient boosting decision tree,” in Visual Communications and Image Processing (VCIP). IEEE, 2016.
- Y. Zhao, Z. Qu, H. Han, and L. Yuan, “An effective and rapid localization algorithm of pupil center based on starburst model,” in Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). IEEE, 2016.
- D. Zhu, S. T. Moore, and T. Raphan, “Robust pupil center detection using a curvature algorithm,” Computer Methods and Programs in Biomedicine, vol. 59, pp. 145–157, 1999.
- D. Liu, Z. Pang, and S. R. Lloyd, “A neural network method for detection of obstructive sleep apnea and narcolepsy based on pupil size and eeg,” IEEE TRANSACTIONS ON NEURAL NETWORKS, vol. 19, no. 2, pp. 308–318, 2008.
- G. Xin, C. Ke, and H. Xiaoguang, “An improved canny edge detection algorithm for color image,” in Industrial Informatics (INDIN), 2012 10th IEEE International Conference on, 2012.
- C. Jayachandra and H. Reddy, “Iris recognition based on pupil using canny edge detection and kmeans algorithm,” in International Journal Of Engineering And Computer Science, vol. 2, no. 1, 2013, pp. 221–225.
- A. B. Roiga, M. Moralesb, J. Espinosaa, J. Pereza, D. Masa, and C. Illueca, “Pupil detection and tracking for analysis of fixational eye micromovements,” Optik, vol. 123, pp. 11–15, 2012.
- C. Yan, Y. Wang, and Z. Zhang, “Robust real-time multi-user pupil detection and tracking under various illumination and large-scale head motion,” Computer Vision and Image Understanding, vol. 115, pp. 1223–1238, 2011.
- S. Chen and J. Epps, “Efficient and robust pupil size and blink estimation from near-field video sequences for human– machine interaction,” IEEE Transactions on Cybernetics, vol. 44, no. 12, pp. 2356–2367, 2014.
- J. N. Sari, H. A. N, L. E. N, P. I. Santosa, and R. Ferdiana, “A study on algorithms of pupil diameter measurement,” in Science and Technology-Computer (ICST), International Conference on, 2016.
- S. Birchfield, Image Processing and Analysis. Cengage Learning, 2016.
- A. S. Saif and M. S. Hossain, “A study of pupil orientation and detection of pupil using circle algorithm::a review,” International Journal of Engineering Trends and Technology (IJETT), vol. 54, no. 1, 2017.
- Mathworks, “vision.cascadeobjectdetector system object.”
- M.-H. Yang, D. J. Kriegman, and N. Ahuja, “Detecting faces in images: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 1, pp. 34–58, 2002.
- A. Al-Rahayfeh and M. Faezipour, “Eye tracking and head movement detection: A state-of-art survey,” IEEE Journal of Translational Engineering in Health and Medicine, 2013.
Index Terms
Keywords