A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Robust Face Detection Using the Hausdorff Distance
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Face Detection and Precise Eyes Location
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Robust Real-Time Face Detection
International Journal of Computer Vision
WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
FloatBoost Learning and Statistical Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust precise eye location under probabilistic framework
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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
Hybrid method based on topography for robust detection of iris center and eye corners
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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This paper is focused on cellular phone embedded eye location system. The proposed eye detection system is based on a hierarchy cascade FloatBoost classifier combined with an MLP neural net post classifier. The system firstly locates the face and eye candidates’ areas in the whole image by a hierarchical FloatBoost classifier. Then geometrical and relative position information of eye-pair and the face are extracted. These features are input to a MLP neural net post classier to arrive at an eye/non-eye decision. Experimental results show that our cellular phone embedded eye detection system can accurately locate double eyes with less computational and memory cost. It runs at 400ms per image of size 256×256 pixels with high detection rates on a SANYO cellular phone with ARM926EJ-S processor that lacks floating-point hardware.