A multi-expert approach for wavelet-based face detection

Authors:
Loris Nanni;Alessandra Lumini
Affiliations:
DEIS, IEIIT - CNR, Universití di Bologna, Viale Risorgimento 2, 40136 Bologna, Italy;DEIS, IEIIT - CNR, Universití di Bologna, Viale Risorgimento 2, 40136 Bologna, Italy
Venue:
Pattern Recognition Letters
Year:
2007

Citing 13
Cited 1

Floating search methods in feature selection

Pattern Recognition Letters
On Combining Classifiers

IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey

IEEE Transactions on Pattern Analysis and Machine Intelligence
Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy

Machine Learning
Robust Face Detection Using the Hausdorff Distance

AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Face Recognition Using Laplacianfaces

IEEE Transactions on Pattern Analysis and Machine Intelligence
An improved BioHashing for human authentication

Pattern Recognition
A multi-expert approach for robust face detection

Pattern Recognition
Face detection using discriminating feature analysis and Support Vector Machine

Pattern Recognition
Multiresolution face recognition

Image and Vision Computing
An automated palmprint recognition system

Image and Vision Computing
AUC: a better measure than accuracy in comparing learning algorithms

AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Face detection using quantized skin color regions merging andwavelet packet analysis

IEEE Transactions on Multimedia

Wavelet decomposition tree selection for palm and face authentication

Pattern Recognition Letters

Quantified Score

Hi-index	0.10

Visualization

Abstract

In this work we present an upright frontal face detection system based on the multi-resolution analysis of the face. The images are decomposed into frequency sub-bands with different levels decomposition using different wavelets. We propose to use a multi-matcher where each matcher (a Radial Basis Function Support Vector Machine) is trained using a different set of features (a given sub-band or the gray values) projected onto a lower subspace by Laplacian Eigenmaps, the matchers are combined using the ''Sum Rule''. The matcher selection is performed by running Sequential Forward Floating Selection. To speed up the detection, an eye detector is used to find the position of the most probable face. Among these sub-windows only the sub-windows that are classified, by the matcher trained using gray values, as ''face'' are classified by the multi-matcher.