A New Segmentation Approach for Ear Recognition
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
A Fast and Fully Automatic Ear Recognition Approach Based on 3D Local Surface Features
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
2D staircase detection using real adaboost
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Shaped wavelets for curvilinear structures for ear biometrics
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Efficient Detection and Recognition of 3D Ears
International Journal of Computer Vision
The image ray transform for structural feature detection
Pattern Recognition Letters
An study on ear detection and its applications to face detection
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
An efficient ear localization technique
Image and Vision Computing
A review of recent advances in 3D ear- and expression-invariant face biometrics
ACM Computing Surveys (CSUR)
A rotation and scale invariant technique for ear detection in 3D
Pattern Recognition Letters
Multibiometric human recognition using 3D ear and face features
Pattern Recognition
ACM Computing Surveys (CSUR)
Entropy based Binary Particle Swarm Optimization and classification for ear detection
Engineering Applications of Artificial Intelligence
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Ear detection from a profile face image is an important step in many applications including biometric recognition. But accurate and rapid detection of the ear for real-time applications is a challenging task, particularly in the presence of occlusions. In this work, a cascaded AdaBoost based ear detection approach is proposed. In an experiment with a test set of 203 profile face images, all the ears were accurately detected by the proposed detector with a very low (5 x 10-6) false positive rate. It is also very fast and relatively robust to the presence of occlusions and degradation of the ear images (e.g. motion blur). The detection process is fully automatic and does not require any manual intervention.