{"title":"Fast and Fully Automatic Ear Detection Using Cascaded AdaBoost","authors":"S. Islam, Bennamoun, Rowan Davies","doi":"10.1109/WACV.2008.4544023","DOIUrl":"https://doi.org/10.1109/WACV.2008.4544023","url":null,"abstract":"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.","PeriodicalId":439571,"journal":{"name":"2008 IEEE Workshop on Applications of Computer Vision","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122628266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Variational Transform Invariant Mixture of Probabilistic PCA","authors":"","doi":"10.1109/WACV.2008.4544001","DOIUrl":"https://doi.org/10.1109/WACV.2008.4544001","url":null,"abstract":"In many video based object recognition applications, the object appearances are acquired by visual tracking and are inconsistent due to misalignments caused by tracking drifting. We believe the misalignments can be removed if we can reduce the inconsistency in the object appearances caused by misalignments through clustering the objects in appearance, space and time domain simultaneously. We therefore propose to learn transform invariant mixtures of probabilistic PCA (TIMPPCA) model from the data while at the same time eliminating the misalignments. The model is formulated in a generative framework, and the misalignments are considered as hidden variables in the model. Variational EM update rules are then derived based on variational message passing (VMP) techniques. The proposed TIMPPCA is applied to improve head pose estimation performance and to detect the change of attention focus in meeting room video for meeting room video indexing/retrieval and achieved promising performance.","PeriodicalId":439571,"journal":{"name":"2008 IEEE Workshop on Applications of Computer Vision","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127626944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}