Recursive neural networks learn to localize faces

Authors:
M. Bianchini;M. Maggini;L. Sarti;F. Scarselli
Affiliations:
Dipartimento di Ingegneria dell'Informazione, Universití degli Studi di Siena, Via Roma, 56-53100 Siena, Italy;Dipartimento di Ingegneria dell'Informazione, Universití degli Studi di Siena, Via Roma, 56-53100 Siena, Italy;Dipartimento di Ingegneria dell'Informazione, Universití degli Studi di Siena, Via Roma, 56-53100 Siena, Italy;Dipartimento di Ingegneria dell'Informazione, Universití degli Studi di Siena, Via Roma, 56-53100 Siena, Italy
Venue:
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
Year:
2005

Citing 6
Cited 8

Detecting Faces in Images: A Survey

IEEE Transactions on Pattern Analysis and Machine Intelligence
Training Support Vector Machines: an Application to Face Detection

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Finding skin in color images

FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Finding Faces in Photographs

ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Supervised neural networks for the classification of structures

IEEE Transactions on Neural Networks
A general framework for adaptive processing of data structures

IEEE Transactions on Neural Networks

2005 Special Issue: Recursive neural networks for processing graphs with labelled edges: theory and applications

Neural Networks - Special issue on neural networks and kernel methods for structured domains
An adaptive counter propagation network based on soft competition

Pattern Recognition Letters
Recursive Neural Networks for Undirected Graphs for Learning Molecular Endpoints

PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
The graph neural network model

IEEE Transactions on Neural Networks
Object recognition using multiresolution trees

SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Recursive neural networks and graphs: dealing with cycles

WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
An eye detection system based on neural autoassociators

ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
Solving graph data issues using a layered architecture approach with applications to web spam detection

Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Localizing faces in images is a difficult task, and represents the first step towards the solution of the face recognition problem. Moreover, devising an effective face detection method can provide some suggestions to solve similar object and pattern detection problems. This paper presents a novel approach to the solution of the face localization problem using Recursive neural networks (RNNs). The proposed method assumes a graph-based representation of images that combines structural and symbolic visual features. Such graphs are then processed by RNNs, in order to establish the possible presence and the position of faces inside the image. A novel RNN model that can deal with graphs with labeled edges has been also exploited. Some experiments on snapshots from video sequences are reported, showing very promising results.