Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection and localization of faces on digital images
Pattern Recognition Letters
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Fast and Accurate Face Detector Based on Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
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)
Robust Face Detection at Video Frame Rate Based on Edge Orientation Features
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Face detection using discriminating feature analysis and Support Vector Machine
Pattern Recognition
A hierarchical neural network for human face detection
Pattern Recognition
Using the idea of the sparse representation to perform coarse-to-fine face recognition
Information Sciences: an International Journal
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Traditional image-based face detection methods use a window based scanning technique where the window is scanned pixel-by-pixel to search for faces in various positions and scales within an image. Therefore, they require high computation cost and are not adequate to the real time applications. In this paper, we introduce a novel coarse-to-fine classification method for image-based face detection using multiple face classifiers. A coarse location of a face is first classified by the gradient feature based face classifier where the window is scanned in large moving steps. From the coarse location of a face, the fine classification is performed to identify the local image as a face using the multiple face classifiers where the window is finely scanned. The multiple face classifiers are designed to take gradient, texture and pixel intensity features and trained by back propagation learning algorithm. Experimental results demonstrate that our proposed method can reduce up to 90.4% of the number of scans compared to the exhaustive full scanning technique and provides the high detection rate.