The eyes have it

Authors:
Terry Riopka;Terrance Boult
Affiliations:
Lehigh University, Bethlehem, PA;Univ. of Colorado at Colorado Springs, Colorado Springs, CO
Venue:
WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
Year:
2003

Citing 3
Cited 9

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ICVS'03 Proceedings of the 3rd international conference on Computer vision systems

Eye localization for face matching: is it always useful and under what conditions?

CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Eye localization in low and standard definition content with application to face matching

Computer Vision and Image Understanding
Robust precise eye location by adaboost and SVM techniques

ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
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A hierarchical floatboost and MLP classifier for mobile phone embedded eye location system

ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Eye perturbation approach for robust recognition of inaccurately aligned faces

AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Experimental evaluation of eye location accuracies and time-lapse effects on face recognition systems

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Classification enhancement via biometric pattern perturbation

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A literature survey on robust and efficient eye localization in real-life scenarios

Pattern Recognition

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Abstract

This paper evaluates the impact of eye localization on face recognition accuracy. To investigate its importance, we present an eye perturbation sensitivity analysis, as well as empirical evidence that reinforces the notion that eye localization plays a key role in the accuracy of face recognition systems. In particular, correct measurement of eye separation is shown to be more important than correct eye location, highlighting the critical role of eye separation in the scaling and normalization of face images. Results suggest that significant gains in recognition accuracy may be achieved by focussing more effort on the eye localization stage of the face recognition process.