![](bg7.png)
Table 2: Principal properties of the techniques adopted. (a:
tolerate movements, b: tolerate glasses, c: independent to
the distance from camera, d: need low computational
resources, e: tolerate occlusions, f: tolerate shadows)
a b c d e f
T1
- n - n y -
T2
y n y - y n
T3
y y n y - n
T4
- n - y y y
T5
n y - - n y
T6
n y y n n n
In Table 3 we present a direct comparison
between the results obtained by the two schemes and
the single eye-detection techniques. We indicated
the competitive scheme with S1 and cooperative
with S2. The single technique (T) is numbered in
according to the order followed in this paper.
For each technique and scheme we report the
mean error and standard deviation in pixels. We
measure the response rate (%) for each technique
and we show the further improvement introduced by
the schemes. Response rate indicate the number of
case in which a technique give a result.
Table 3: Experimental results (E: mean error; σ: standard
deviation; %: response rate).
S1 S2 T1 T2 T3 T4 T5 T6
E
6,1 6,4 8,8 6,5 8,1 6,7 12,9 11,5
σ
6,3 6,5 8,2 6,0 7,5 6,5 9,2 6,8
%
98,1 95,8 87,0 72,2 76,4 68,2 59,2 41,4
The results show that the schemes implemented
reduce considerably mean error of the single
techniques. The schemes also provide more
continuity and robustness in offering results,
contrarily to the single techniques.
5 CONCLUSIONS
In this paper, we proposed an innovative approach to
the problem of the Eye-Tracking. Traditional eye-
detectors, chosen for its properties, are merged by
two different schemes (competitive and cooperative
scheme). The described approach features high
reliability and high robustness to noise and bad
illumination. To illustrate our work, we introduced
a proof-of-concept single camera remote eye-tracker
and discussed its implementation and the obtained
experimental results. More applications of the
proposed approach are currently being investigated
in our Lab to portable, handheld and wearable
computers. At the moment, the main issues being
dealt with are computational cost and power
consumption reduction. Finally, we are realizing a
comparative study on a number of (more)
sophisticated different cooperative schemes to obtain
a further improvement in accuracy and reliability.
REFERENCES
Viola, P., Jones, M., 2001. Rapid Object Detection using a
Boosted Cascade of Simple Features. In CVPR’01,
International Conference on Computer Vision And
Pattern Recognition. IEEE Computer Science Society.
Lienhart, R., Maydt, J., 2002. An Extended Set of Haar-
like Features for Rapid Object Detection. In ICIP’02,
International Conference on Image Processing. IEEE
Signal Processing Society.
Freund, Y., Schapire, R., 1996. Experiments with a new
boosting algorithm. In ICML’96, International
Conference on Machine Learning. Morgan Kaufmann.
Gonzalez, R., Woods, R., 2002. Digital Image Processing.
Prentice Hall Press, 2
nd
edition.
Freund, Y., Schapire, R., 1997. A Decision-Theoretic
Generalization of On-Line learning and an application
to boosting. Journal of Computer and System
Sciences, Elsevier Inc.
Hsu, R., Abdel-Mottaleb, M., Jain, A., 2002. Face
detection in color images. Transactions on Pattern
Analysis and Machine Intellegence. IEEE Computer
Science Society.
Coifman, B., Beymer, D., McLauchlan, P., Malik, J.,
1998. A real-time computer vision system for vehicle
tracking and traffic surveillance. Transportation
Research Part C 6. Elsevier Inc.
Babcock, J., Pelz, J., 2004. Building a lightweight
eyetracking headgear. In ETRA’04, International
Symposium on Eye Tracking Research and
Applications. ACM Press.
Ji, Q., Zhu, Z. 2004. Eye and gaze tracking for interactive
graphic display. Machine Vision and Applications.
Springer.
Tomasi, C., Kanade, T., 1992. Shape and motion from
image streams under orthography: a factorization
method. International Journal of Computer Vision.
Springer.
Black, M., Jepson, A., 1998. EigenTracking: Robust
Matching and Tracking of Articulated Objects Using a
View-Based Representation. International Journal of
Computer Vision. Springer.
Morimoto, C., Koons, D., Amir, A., Flickner M., 1998.
Pupil detection and tracking using multiple light
sources. Technical Report RJ-10117, IBM Almaden
Research Center.
Morimoto, C., Koons, D., Amir, A., Flickner M., 1998.
Real-time detection of eyes and faces. In PUI’98
International Workshop on Perceptual User
Interfaces. ACM Press.
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