In view of the existing problems in fabricating auricular prostheses, such as the low accuracy of contours, the complexity of incorporation prosthesis into tissue area surrounding the deficient side etc.

Automatic ear recognition based on contour curve and local feature;

Research of the Ear Recognition Based on Principal Component Analysis;

Selection of the anchor points and design of the detecting algorithm for ear recognition system;

Two ear extraction and normalization methods, the mark line (long axis of the outer ear contour) based method and the mark points (the start and end points of the outer ear contour) based method, were proposed for recognizing ear images in the USTB ear database.

According to the feature of ear image and requirement of extraction, this paper proposes a new edge detection method based on contour composition.

Normalization of ear image has a very important significance in ear recognition,and provides precondition for later work.

On the basis of comparing to other existing approaches,this paper introduces a kind of fast ear detecting and tracking approach under the complex background,which has two stages: off-line cascaded classifier training and on-line ear detection.

Finally, we utilized the module of ear detection tojudge whether to contain the ears of people in the candidatearea or not, and obtaine.

As compared to other existing approaches,this paper introduces a fast ear detection approach under the complex background which has two stages:off-line cascaded classifier training and on-line ear detection.