A COMPUTER VISION APPROACH FOR THE EYE ACCESSING CUE MODEL USED IN NEURO-LINGUISTIC PROGRAMMING

This paper investigates the Eye Accessing Cue (EAC) model used in NeuroLinguistic Programming (NLP) and shows how image processing techniques can be used to improve the interpretation of this model. An experiment was carried out to validate the model by inducing certain eye cues using a set of questions. A simple and efficient method is proposed for automatically locating the eyes and the correspondent EAC. The relative position between the iris and the sclera is determined using a fast mechanism, based on the analysis of integral projections inside the bounding box of the eye.

[1]  Test of the Eye-Movement Hypothesis of Neurolinguistic Programming , 1980, Perceptual and motor skills.

[2]  Takeo Kanade,et al.  Recognizing upper face action units for facial expression analysis , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[3]  Richard W. Thompson,et al.  Mental imagery as revealed by eye movements and spoken predicates: A test of neurolinguistic programming. , 1985 .

[4]  Andreas Lanitis.  On the significance of different facial parts for automatic age estimation , 2002, 2002 14th International Conference on Digital Signal Processing Proceedings. DSP 2002 (Cat. No.02TH8628).

[5]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[6]  Corneliu Florea,et al.  A fast method for detecting eye accessing cues used in Neuro-Linguistic Programming , 2011, 2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing.

[7]  Klaus J. Kirchberg,et al.  Robust Face Detection Using the Hausdorff Distance , 2001, AVBPA.

[8]  Eye-Movements and Ongoing Task Processing , 2003, Perceptual and motor skills.

[9]  J. Cunningham,et al.  Hypothesized Eye Movements of Neurolinguistic Programming: A Statistical Artifact , 1985, Perceptual and motor skills.

[10]  Zhi-Hua Zhou,et al.  Projection functions for eye detection , 2004, Pattern Recognit..

[11]  Nicu Sebe,et al.  Communication and Automatic Interpretation of Affect from Facial Expressions , 2010 .

[12]  Georgios Diamantopoulos,et al.  Novel eye feature extraction and tracking for non-visual eye-movement applications , 2010 .

[13]  Qiang Ji,et al.  In the Eye of the Beholder: A Survey of Models for Eyes and Gaze , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  C. E. Beck,et al.  Test of the Eye-Movement Hypothesis of Neurolinguistic Programming: A Rebuttal of Conclusions , 1984 .

[15]  R. Carpenter,et al.  Movements of the Eyes , 1978 .

[16]  Juliet Grayson,et al.  Neuro-Linguistic Programming , 2000 .

[17]  A study of eye movement patterns in the Neuro-Linguistic Programming Model , 1981 .

[18]  S A Poffel,et al.  Neurolinguistic Programming: A Test of the Eye-Movement Hypothesis , 1985, Perceptual and motor skills.

[19]  Weixing Wang,et al.  Driver Fatigue Detection Based on Eye Tracking , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[20]  M. Buckner,et al.  Eye movement as an indicator of sensory components in thought. , 1987 .

[21]  Aleix M. Martinez,et al.  The AR face database , 1998 .

[22]  Zhihong Zeng,et al.  A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions , 2009, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Richard Bandler,et al.  Frogs into princes : neuro linguistic programming , 1979 .

[24]  Takeo Kanade,et al.  Picture Processing System by Computer Complex and Recognition of Human Faces , 1974 .