Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202528249 ISBN 13: 9786202528245
Da: preigu, Osnabrück, Germania
EUR 47,20
Quantità: 5 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. Modular Independent Component Analysis Approach for Face Recognition | Kailash Karande (u. a.) | Taschenbuch | 112 S. | Englisch | 2020 | LAP LAMBERT Academic Publishing | EAN 9786202528245 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing Apr 2020, 2020
ISBN 10: 6202528249 ISBN 13: 9786202528245
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 54,90
Quantità: 2 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In the traditional ICA method the entire face image is considered for holistic approach of face recognition, hence large variation in pose or illumination will affect the recognition rate profoundly. In this approach dividing the face image in sub-images, independent components are obtained on these sub-images and used for face recognition. Here we have explored modular ICA approach with partition of facial images as well as with local facial components such as eyes, nose and mouth. The face recognition task affects due to presence of noise in facial images. We have experimented ICA algorithms for reduction of noise from facial images so as to reduce noise effect. The research work presented in this book and methods proposed for face recognition are unique and definitely will provide new way of analyzing facial features. This will be a good contribution for research in biometrics and image processing field. 112 pp. Englisch.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202528249 ISBN 13: 9786202528245
Da: moluna, Greven, Germania
EUR 45,45
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Karande KailashKarande Kailash Jagannath has completed his PhD in Electronics and Telecommunication from SRTM University, Nanded, India. He has total 100+ Publications in International and National Journals and Conferences. He has 5 .
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing Apr 2020, 2020
ISBN 10: 6202528249 ISBN 13: 9786202528245
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 54,90
Quantità: 1 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In the traditional ICA method the entire face image is considered for holistic approach of face recognition, hence large variation in pose or illumination will affect the recognition rate profoundly. In this approach dividing the face image in sub-images, independent components are obtained on these sub-images and used for face recognition. Here we have explored modular ICA approach with partition of facial images as well as with local facial components such as eyes, nose and mouth. The face recognition task affects due to presence of noise in facial images. We have experimented ICA algorithms for reduction of noise from facial images so as to reduce noise effect. The research work presented in this book and methods proposed for face recognition are unique and definitely will provide new way of analyzing facial features. This will be a good contribution for research in biometrics and image processing field.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 112 pp. Englisch.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202528249 ISBN 13: 9786202528245
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 55,56
Quantità: 1 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In the traditional ICA method the entire face image is considered for holistic approach of face recognition, hence large variation in pose or illumination will affect the recognition rate profoundly. In this approach dividing the face image in sub-images, independent components are obtained on these sub-images and used for face recognition. Here we have explored modular ICA approach with partition of facial images as well as with local facial components such as eyes, nose and mouth. The face recognition task affects due to presence of noise in facial images. We have experimented ICA algorithms for reduction of noise from facial images so as to reduce noise effect. The research work presented in this book and methods proposed for face recognition are unique and definitely will provide new way of analyzing facial features. This will be a good contribution for research in biometrics and image processing field.