Notice of RetractionImproving adaboost ear detection with skin-color model and multi-template matching

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

As an essential stage of human ear recognition, Ear detection has a direct and important impact on final recognition performance. Traditional Adaboost algorithm based human ear detection method has some inherent drawbacks will lead to imperfect ear detection, such as the long time training, overly dependent on ear samples quality, etc. Therefore, to overcome such problems partially, the strategies of YCbCr skin-color filtering and multi-template matching are introduced to get more accurate ear location under occasions of insufficient training and bad initial ear position. The proposed method can eliminate most cases of false positing and multi-location or incomplete selection. Experimental results show the proposed method is robust and effective not only in static image but also under dynamic environments.