| 普适计算与人机交互 | 
									
										
		  							 | 
          							
		  									  								 
		  									  							    
		  									  								 
		  									  							 | 
        						 
      						 
      					 | 
  					 
  					
    					 | 
   					 
   										
    					| 移动设备眼动跟踪技术 | 
  					 
  					  										
						| 程时伟, 陆煜华, 蔡红刚 | 
					 
															
					| 浙江工业大学 计算机科学与技术学院, 浙江 杭州 310023 | 
					 
										
						 | 
					 
   										
    					| Mobile device based eye tracking technology | 
  					 
  					  					  					
						| CHENG Shi wei, LU Yu hua, CAI Hong gang | 
					 
															
						| School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China | 
					   
									 
				
				
					
						
							
								
									
									
									
									
									 
          
          
            
                         
            
									            
									                
																														  
																|   [1] GARCIA D, SINTOS I. EyeDROID: Gaze tracking component for Android SPCLAutumn 2014[J/OL]. [20150513].http:∥www.itu.dk/~tped/teaching/pervasive/SPCLE2014/draft01handins/02_EyeDROID%20Gaze%20tracking%20component%20for%20Android.pdf. 
[2] NAGAMATSU T, YAMAMOTO M, SATO H. MobiGaze: development of a gaze interface for handheld mobile devices [C]∥ CHI′10 Extended Abstracts on Human Factors in Computing Systems. New York: ACM, 2010: 3349-3354. 
[3] LUKANDER K. Measuring gaze point on handheld mobile devices [C]∥ CHI′04 Extended Abstracts on Human Factors in Computing Systems. New York: ACM, 2004: 1556-1556. 
[4] DEUBEL H, BRIDGEMAN B. Fourth Purkinje image signals reveal eyelens deviations and retinal image distortions during saccades [J]. Vision Research, 1995,35(4): 529-538. 
[5] 程时伟,孙志强.用于移动设备人机交互的眼动跟踪方法[J].计算机辅助设计与图形学学报, 2014, 26(8): 1354-1361. 
CHENG Shiwei, SUN Zhiqiang. An approach to eye tracking for mobile device based interaction [J]. Journal of ComputerAided Design and Computer Graphics, 2014, 26(8): 1354-1361. 
[6] MARIAKAKIS A, GOEL M, AUMI M T I, et al. SwitchBack: using focus and saccade tracking to guide users’ attention for mobile task resumption[C]∥ Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI). New York: ACM, 2015: 2953-2962. 
[7] KAO C W, CHEN Y W, YANG C W, et al. Eye gaze tracking based on pattern voting scheme for mobile device [C]∥ Proceedings of International Conference on Instrumentation, Measurement, Computer, Communication and Control. New York: IEEE, 2011: 337-340. 
[8] MILUZZO E, WANG T, CAMPBELL A T. EyePhone: activating mobile phones with your eyes [C]∥ Proceedings of the Second ACM SIGCOMM Workshop on Networking, Systems, and Applications on Mobile Handhelds. New York: ACM, 2010: 1520. 
[9] PINO C, KAVASIDIS I. Improving mobile device interaction by eye tracking analysis [C]∥ Proceedings of Federated Conference on Computer Science and Information Systems (FedCSIS). New York: IEEE, 2012: 1199-1202. 
[10] WOOD E, BULLING A. Eyetab: modelbased gaze estimation on unmodified tablet computers [C]∥ Proceedings of the Symposium on Eye Tracking Research and Applications (ETRA). New York: ACM, 2014: 207-210. 
[11] OJALA T, PIETIKAINEN M, MAENPAA T. Multiresolution grayscale and rotation invariant texture classification with local binary patterns [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971-987. 
[12] LI W, FU P, ZHOU L. Face recognition method based on dynamic threshold local binary pattern [C]∥ Proceedings of the 4th International Conference on Internet Multimedia Computing and Service. New York: ACM, 2012: 20-24. 
[13] SONG E, PAN N, HUNG C C, et al. Reflection invariant local binary patterns for image texture classification [C]∥ Proceedings of the 2015 Conference on Research in Adaptive and Convergent Systems. New York: ACM, 2015: 210-215. 
[14] EBADI T, KUKENYS I, BROWNE W N, et al. Humaninterpretable feature pattern classification system using learning classifier systems [J]. Evolutionary Computation, 2014, 22(4): 629-650. 
[15] VIOLA P, JONES M. Rapid object detection using a boosted cascade of simple features [C]∥ Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2001: 1511-1518. 
[16] FREUND Y. Boosting a weak learning algorithm by majority [J]. Information and Computation, 1995,121(2): 256-285. 
[17] MAHALINGAM G, KAMBHAMETTU C. Face verification with aging using AdaBoost and local binary patterns [C]∥ Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing. New York: ACM, 2010: 101-108. 
[18] LIENHART R, MAYDT J. An extended set of Haarlike features for rapid object detection[C]∥ Proceedings of 2002 International Conference on Image Processing. New York: IEEE, 2002: 1900-1903. 
[19] REZAEI M, KLETTE R. Adaptive Haarlike classifier for eye status detection under nonideal lighting conditions [C]∥ Proceedings of the 27th Conference on Image and Vision Computing New Zealand. New York: ACM, 2012: 521-526. 
[20] TENG Z, ZHANG B, LIU F. Railway region detection based on Haarlike features[C]∥ Proceedings of International Conference on Internet Multimedia Computing and Service. New York: ACM, 2014: 121. 
[21] 刘宝生, 闫莉萍, 周东华. 几种经典相似性度量的比较研究[J].计算机应用研究, 2006, 23(11): 13. 
LIU Baosheng, YAN Liping, ZHOU Donghua. Comparison of some classical similarity measures [J]. Application Research of Computers, 2006, 23(11): 13.  | 
															   
																													 
									             
									           
             
			            			 
			 
             
												
											    	
											        	 | 
											        	Viewed | 
											         
													
											        	 | 
											        	 | 
											         
											      	
												         | 
												        
												        	Full text 
												          	
												         | 
											        	
												        	
												        	 
												        	
												          	 
												          	
												          	
														 | 
													 
													
												         | 
												         | 
													 
													
												         | 
												        
												        	Abstract 
												          	
														 | 
												        
															
															 
															
															
												         | 
													 
													
												         | 
												         | 
													 
													
												         | 
												        Cited  | 
												        
												        	
												         | 
													 
													
												         | 
												         | 
												         | 
													 
													
													    |   | 
													    Shared | 
													       | 
												  	 
												  	
													     | 
													     | 
													     | 
											  		 
											  		
													    |   | 
													    Discussed | 
													       | 
												  	 
											 
											 
             
           
      
									
									
		
									
									
									
									
									
									 | 
								 
							 
						 | 
					 
				 
			
		 |