Conference Paper

A Hybrid Fuzzy Approach for Human Eye Gaze Pattern Recognition

DOI: 10.1007/978-3-642-03040-6_80 Conference: Advances in Neuro-Information Processing, 15th International Conference, ICONIP 2008, Auckland, New Zealand, November 25-28, 2008, Revised Selected Papers, Part II
Source: DBLP

ABSTRACT

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.

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    • "ta was pre-processed by several signal processing routines, resulting in 80 spectral power density values serving as inputs to a LVQ net. [20] applied neural networks to validate if the way a person reads influences the way he understands information and proposed a novel method of detecting the level of engagement in reading based on gaze-patterns. [21] applied their hybrid fuzzy signatures with Levenberg-Marquardt optimization method for recognizing different eye-gaze patterns while viewing faces or text documents. All these approaches have in common that the gaze data needs a high amount of pre-processing before it can serve as input to the neural networks. In a previous study, we sh"
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