Digital image processing
Genetic Model Optimization for Hausdorff Distance-Based Face Localization
ECCV '02 Proceedings of the International ECCV 2002 Workshop Copenhagen on Biometric Authentication
Rotation Invariant Neural Network-Based Face Detection
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Neural network-based face detection
Neural network-based face detection
A statistical approach to 3d object detection applied to faces and cars
A statistical approach to 3d object detection applied to faces and cars
Optimization of a training set for more robust face detection
Pattern Recognition
Research on eye-state based monitoring for drivers' dozing
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Expand training set for face detection by GA re-sampling
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Detection and tracking faces in unconstrained color video streams
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
How to train a classifier based on the huge face database?
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Face samples re-lighting for detection based on the harmonic images
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
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This paper presents a novel approach to the real-time detection of frontal faces in static grey images. The faces may be rotated in-plane from -60° to 60° which suffices for most practical applications. The detector consists of a multi-path decision tree, with two different types of node classifiers. Each tree node has the capability to reject the current subwindow or to classify it in one out of 3 rotation classes. The node classifiers are designed so that most of the non-face locations are eliminated at early nodes, leaving the harder cases to later nodes. The improvement at each node is twofold, higher angular resolution and successive rejection of non-face locations. The early nodes consist of sparse linear feature nets defined in the gradient direction domain which are very efficient to compute. So many of the non-face locations are rejected with low computational effort. The classifiers in the last node apply linear feature nets using spatially sampled grey value features. The proposed detector design allows to focus onto interesting image regions very rapidly allowing for a real-time system on standard PCs which can process approximately 25 frames per second. We present experimental results of the system on the widely used CMU Database (frontal and rotated set) and the BioID Database where an overall detection rate of 91% is achieved.