研究生: |
盧姿卉 |
---|---|
論文名稱: |
可應用於一般課堂環境中之人眼開闔狀狀態研究 Eye State Recognition with Application in the Classroom |
指導教授: | 李忠謀 |
學位類別: |
碩士 Master |
系所名稱: |
資訊工程學系 Department of Computer Science and Information Engineering |
論文出版年: | 2013 |
畢業學年度: | 101 |
語文別: | 中文 |
論文頁數: | 49 |
中文關鍵詞: | 人臉偵測 、眼睛偵測 、眼睛開闔辨識 、熵 、雜度度函數 |
英文關鍵詞: | face detection, eye detection, eye state recognition, Entropy, Complexity Function |
論文種類: | 學術論文 |
相關次數: | 點閱:96 下載:8 |
分享至: |
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
眼睛開闔辨識是電腦視覺的一個重要技術,能夠在生活中發展成多種應用,大部分的眼睛狀態偵測,環境皆屬於背景較為單純、近距離以及頭部晃動不大的情形,像是汽車駕駛疲勞偵測系統,然而本研究希望能將眼睛開闔辨識應用於一般課堂環境中,因此需要解決在有光線干擾及遠距離低解析度下的環境中,仍能快速且有效辨識眼睛的開闔狀態。
本研究之方法共分成三個部分,分別是人臉偵測、眼睛區域決策,最後則是眼睛狀態辨識。首先對影像做人臉偵測,接著將做完前處理的臉部影像利用局部取像的方法得到眼睛的大致位置,再利用水平投影及垂直投影找出眼睛精確的範圍及位置,最後本研究利用開闔眼睛影像輪廓複雜度之差異設計一套新的特徵擷取方式,並搭配已事前訓練過的SVM模型來判斷眼睛的開闔狀態。
無論是近距離或是遠距離實驗,由實驗結果可證明出在相同的辨識率下,本研究所設計之特徵擷取方式比複雜度函數的方法能判斷出的開閉眼資料比例多,因此整體的執行時間可以降低,也證明了本篇方法的可用性,除了開閉眼整體辨識率皆可達到84.9%以上,且隨著門檻值的調整,執行時間也可比單純用SVM快了1.5至3倍,時間上的減少能帶給本系統很大的效益。
Eye state recognition is an important technology in the computer vision. It can be developed to variety applications. Most eye state recognition is pure background, short distance, and the head does not shack. Due to the application in the general classroom that is light interference and long distance, the purpose of our research is to recognize the eye state quickly and effectively.
Our method is divided into three parts, face detection, eye region decision, and eye state recognition. First is to find out the face image and do the pre-processing, then make use of the area of interest (AOI) to get the roughly eye position, the last step is utilizing the horizontal projection and vertical projection to get the precise eye position. Eye state recognition is using our proposed method that is a new way to extract feature from binary image and work with SVM model to determine the eye state.
The experiment shows that our proposed method that is a new way to extract feature from binary image is better than complexity function method. And our method is not only performs well in the recognition rate but also in the execution time that is 1.5~3 times faster than SVM method.
[1] Y.-S. Wu, T.-W. Lee, Q.-Z. Wu, and H.-S. Liu, "An eye state recognition method for drowsiness detection," Vehicular Technology Conference, pp. 1-5, 2010.
[2] H. Wang, L. Zhou, and Y. Ying, "A novel approach for real time eye state detection in fatigue awareness system," Robotics Automation and Mechatronics, pp. 528-532, 2010.
[3] Y. Wang and B. Yuan, "A novel approach for human face detection from color images under complex background," Pattern Recognition, vol. 34, pp. 1983-1992, 2001.
[4] C. Garcia and G. Tziritas, "Face detection using quantized skin color regions merging and wavelet packet analysis," Multimedia, vol. 1, pp. 264-277, 1999.
[5] R.-L. Hsu, M. Abdel-Mottaleb, and A. K. Jain, "Face detection in color images," Pattern Analysis and Machine Intelligence, vol. 24, pp. 696-706, 2002.
[6] C. Lin, "Face detection in complicated backgrounds and different illumination conditions by using YCbCr color space and neural network," Pattern Recognition Letters, vol. 28, pp. 2190-2200, 2007.
[7] F. Marqués and V. Vilaplana, "A morphological approach for segmentation and tracking of human faces," Pattern Recognition, pp. 1064-1067, 2000.
[8] K.-M. Lam and H. Yan, "Locating and extracting the eye in human face images," Pattern recognition, vol. 29, pp. 771-779, 1996.
[9] M. Turk and A. Pentland, "Eigenfaces for recognition," Journal of cognitive neuroscience, vol. 3, pp. 71-86, 1991.
[10] H. A. Rowley, S. Baluja, and T. Kanade, "Neural network-based face detection," Pattern Analysis and Machine Intelligence, vol. 20, pp. 23-38, 1998.
[11] A. N. Rajagopalan, K. S. Kumar, J. Karlekar, R. Manivasakan, M. M. Patil, U. B. Desai, et al., "Finding faces in photographs," Computer Vision, pp. 640-645, 1998.
[12] P. Viola and M. J. Jones, "Robust real-time face detection," International journal of computer vision, vol. 57, pp. 137-154, 2004.
[13] 簡郁菱, "可應用於學生專注度之人眼開闔偵測研究," 國立臺灣師範大學, 2012.
[14] Z.-H. Zhou and X. Geng, "Projection functions for eye detection," Pattern recognition, vol. 37, pp. 1049-1056, 2004.
[15] R. Valenti and T. Gevers, "Accurate eye center location and tracking using isophote curvature," Computer Vision and Pattern Recognition, pp. 1-8, 2008.
[16] J. Ren and X. Jiang, "Fast eye localization based on pixel differences," Image Processing, pp. 2733-2736, 2009.
[17] Y. Wu, H. Liu, and H. Zha, "A new method of detecting human eyelids based on deformable templates," Systems, Man and Cybernetics, pp. 604-609, 2004.
[18] I. Fasel, B. Fortenberry, and J. Movellan, "A generative framework for real time object detection and classification," Computer Vision and Image Understanding, vol. 98, pp. 182-210, 2005.
[19] H. Tan and Y.-J. Zhang, "Detecting eye blink states by tracking iris and eyelids," Pattern Recognition Letters, vol. 27, pp. 667-675, 2006.
[20] M. Dehnavi, N. Attarzadeh, and M. Eshghi, "Real time eye state recognition," Electrical Engineering, pp. 1-4, 2011.
[21] 林國暐 and 陳良驊, "智慧型人眼狀態偵測系統," 龍華科技大學,2010.
[22] C. Xu, Y. Zheng, and Z. Wang, "Efficient eye states detection in real-time for drowsy driving monitoring system," Information and Automation, pp. 170-174, 2008.
[23] T. Ojala, M. Pietikainen, and T. Maenpaa, "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns," Pattern Analysis and Machine Intelligence, vol. 24, pp. 971-987, 2002.
[24] N. Otsu, "A threshold selection method from gray-level histograms," Automatica, vol. 11, pp. 23-27, 1975.
[25] Z. Tian and H. Qin, "Real-time driver's eye state detection," Vehicular Electronics and Safety, pp. 285-289, 2005.
[26] T. Hong, H. Qin, and Q. Sun, "An improved real time eye state identification system in driver drowsiness detection," Control and Automation, pp. 1449-1453, 2007.
[27] Y.-K. Chen, T.-Y. Cheng, and S.-T. Chiu, "Motion Detection with Entropy in Dynamic Background," Control, Automation and Robotics, pp. 263-266, 2009.
[28] T. Joachims, "Making large scale SVM learning practical," 1999.
[29] C. J. Burges, "A tutorial on support vector machines for pattern recognition," Data mining and knowledge discovery, vol. 2, pp. 121-167, 1998.
[30] C.-C. Chang and C.-J. Lin, "LIBSVM: a library for support vector machines," Intelligent Systems and Technology, vol. 2, p. 27, 2011.