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- W61444161 abstract "Department of Computer Science, The Academic College of Tel-Aviv Yaffo, Tel-Aviv 64044,Israel fY aniv.Ninio@amdocs.com gAbstract. One of the main challenges in 'real-life' object recognition applica-tionsiskeepingsomeinvariancepropertiessuchas:translation,scaling,androta-tion.However,tryingtomaintainsuchinvariantscanimpairrecognitioncapabili-ties,especiallywhenthefamilyofobjectsofinteresthasalargeshapevariability.We present a general family of shape metrics that generalizes Procrustes metricand within this framework learns the desired shape metric parameters from la-beled training samples. The learnt distance retains invariance properties on onehand and emphasizes the discriminative shape features on the other hand. Weshow how these metrics can be incorporated in multi-class classication kernelSVMs. We demonstrate the merits of this approach on multi-class facial expres-sions recognition using the AR dataset. The results address some questions andcautions regarding the interpretation of classication results when using still im-ages datasets collected in a controlled lab environment and their relevance for'real-life' applications." @default.
- W61444161 created "2016-06-24" @default.
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- W61444161 date "2008-01-01" @default.
- W61444161 modified "2023-09-27" @default.
- W61444161 title "Learning Facial Expressions: From Alignment to Recognition" @default.
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