Advances in Neuro-Information Processing

Volume 5507 of the series Lecture Notes in Computer Science pp 655-662

A Hybrid Fuzzy Approach for Human Eye Gaze Pattern Recognition

  • Dingyun ZhuAffiliated withSchool of Computer Science
  • , B. Sumudu U. MendisAffiliated withSchool of Computer Science
  • , Tom GedeonAffiliated withSchool of Computer Science
  • , Akshay AsthanaAffiliated withSchool of Engineering, The Australian National University
  • , Roland GoeckeAffiliated withSchool of Engineering, The Australian National University

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Face perception and text reading are two of the most developed visual perceptual skills in humans. Understanding which features in the respective visual patterns make them differ from each other is very important for us to investigate the correlation between human’s visual behavior and cognitive processes. We introduce our fuzzy signatures with a Levenberg-Marquardt optimization method based hybrid approach for recognizing the different eye gaze patterns when a human is viewing faces or text documents. Our experimental results show the effectiveness of using this method for the real world case. A further comparison with Support Vector Machines (SVM) also demonstrates that by defining the classification process in a similar way to SVM, our hybrid approach is able to provide a comparable performance but with a more interpretable form of the learned structure.