Face localization by neural networks trained with Zernike moments and Eigenfaces feature vectors. A comparison - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2007

Face localization by neural networks trained with Zernike moments and Eigenfaces feature vectors. A comparison

Résumé

Face localization using neural network is presented in this communication. Neural network was trained with two different kinds of feature parameters vectors; Zernike moments and Eigenfaces. In each case, coordinate vectors of pixels surrounding faces in the images were used as target vectors on the supervised training procedure. Thus, trained neural network provides on its output layer a coordinates vector (R,θ) representing pixels surrounding the face contained in treated image. This way to proceed gives accurate faces contours which are well adapted to the faces shapes. Performances obtained for the two kinds of training feature parameters were recorded using a quantitative measurement criterion according to experiences carried out on the XM2VTS database.
Fichier principal
Vignette du fichier
AVSS2007.pdf (411.15 Ko) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte
Loading...

Dates et versions

hal-00147392 , version 1 (16-05-2007)

Identifiants

Citer

Mohammed Saaidia, Anis Chaari, Sylvie Lelandais, Vincent Vigneron, Mouldi Bedda. Face localization by neural networks trained with Zernike moments and Eigenfaces feature vectors. A comparison. 2007 IEEE International Conference on Advanced Video and Signal based Surveillance, Sep 2007, London, United Kingdom. 6 p., ⟨10.1109/AVSS.2007.4425340⟩. ⟨hal-00147392⟩
115 Consultations
171 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More