In this paper we propose an integrated system for face detection and face recognition based on improved versions of state-of-the-art statistical learning techniques such as Boosting and LDA. Both the detection and the recognition processes are performed on facial features (e.g., the eyes, the nose, the mouth, etc) in order to improve the recognition accuracy and to exploit their statistical independence in the training phase. Experimental results on real images show the superiority of our proposed techniques with respect to the existing ones in both the detection and the recognition phase. © 2009 Springer Berlin Heidelberg.
Improved statistical techniques for multi-part face detection and recognition / Christian, Micheloni; Sangineto, Enver; Cinque, Luigi; Gian Luca, Foresti. - 5575 LNCS:(2009), pp. 331-340. (Intervento presentato al convegno 16th Scandinavian Conference on Image Analysis, SCIA 2009 tenutosi a Oslo nel 15 June 2009 through 18 June 2009) [10.1007/978-3-642-02230-2_34].
Improved statistical techniques for multi-part face detection and recognition
SANGINETO, Enver;CINQUE, LUIGI;
2009
Abstract
In this paper we propose an integrated system for face detection and face recognition based on improved versions of state-of-the-art statistical learning techniques such as Boosting and LDA. Both the detection and the recognition processes are performed on facial features (e.g., the eyes, the nose, the mouth, etc) in order to improve the recognition accuracy and to exploit their statistical independence in the training phase. Experimental results on real images show the superiority of our proposed techniques with respect to the existing ones in both the detection and the recognition phase. © 2009 Springer Berlin Heidelberg.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.