A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision, Graphics, and Image Processing
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Projection based method for segmentation of human face and its evaluation
Pattern Recognition Letters
WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
Robust Real-Time Face Detection
International Journal of Computer Vision
A new approach for fast face detection
NN'06 Proceedings of the 7th WSEAS International Conference on Neural Networks
A simple and effective real-time eyes detection human detection without training procedure
SSIP'06 Proceedings of the 6th WSEAS International Conference on Signal, Speech and Image Processing
Automatic detection of face and facial features
ISPRA'08 Proceedings of the 7th WSEAS International Conference on Signal Processing, Robotics and Automation
Automatic facial feature extraction by genetic algorithms
IEEE Transactions on Image Processing
A new accurate technique for iris boundary detection
WSEAS Transactions on Computers
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In this study, a computational algorithm has been developed to automatically detect human face and irises from color images captured by real-time camera. Haar cascade-based algorithm has been applied for simple and fast face detection. The face image is then converted into grayscale image. Three types of image processing techniques have been tested respectively to study its effect on the performance of iris detection algorithm. Then, iris candidates are extracted from the valley created at the face region. The iris candidates are paired up and the cost of each possible pairing is computed by a combination of mathematical models. Finally, the positions of the detected irises are used as a reference to refine the face region. The algorithm has been tested by quality images from Logitech camera and noisy images from Voxx CCD camera. The proposed algorithm has achieved 83.60% as the highest success rate of iris detection under a user-friendly and unconstraint office environment.