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An unsupervised domain adaptation concept for face recognition applications

: Herrmann, Christian

Volltext urn:nbn:de:0011-n-4175642 (766 KByte PDF)
MD5 Fingerprint: 041616d4dd5e394ca8c33c1157ef4240
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Erstellt am: 25.10.2016

Beyerer, Jürgen (Ed.) ; Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung -IOSB-, Karlsruhe; Karlsruhe Institute of Technology -KIT-, Lehrstuhl für Interaktive Echtzeitsysteme -IES-:
Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory 2015. Proceedings : July, 19 to 26, Triberg-Nussbach, Germany
Karlsruhe: KIT Scientific Publishing, 2016 (Karlsruher Schriften zur Anthropomatik 24)
ISBN: 978-3-7315-0519-8
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation and Institute for Anthropomatics, Vision and Fusion Laboratory (Joint Workshop) <2015, Triberg-Nussbach>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

When facing real world data, domain shift is a significant challenge for face recognition methods. A domain mismatch between the datasets used to train a recognition model in the lab and the target data leads to a negative impact on the performance of the recognitions system. Because it is difficult for some domains, like surveillance footage, to collect sufficiently large training datasets, a concept to generalize from data originating from a set of given domains to novel domains is proposed. The critical difference to most existing learning strategies under these conditions is the additional constraint that no data is available from the target domain which is called unsupervised domain adaptation. The suggested concept combines large-margin dimension reduction with an SVM-based framework for unsupervised domain adaptation. This unified strategy benefits by using more of the available knowledge about the training data in the sense of dataset/domain membership of the training samples compared to simply combining all available training datasets into one large dataset.