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

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.

• Type: Conference
• Year: 2007
• Where: AVSS
• Authors: Mohammed Saaidia, Anis Chaari, Sylvie Lelandais, Vincent Vigneron, Mouldi Bedda
• Keywords: AVSS 2007, Feature Parameters Vectors, Neural Network, Pixels Surrounding Faces, Signal Processing