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
Combining Features for Recognizing Emotional Facial Expressions in Static Images
Verbal and Nonverbal Features of Human-Human and Human-Machine Interaction
The new italian audio and video emotional database
COST'09 Proceedings of the Second international conference on Development of Multimodal Interfaces: active Listening and Synchrony
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The multi-scale search based face detection is essential to use a window scanning technique where the window is scanned pixel-by-pixel to search for faces in various positions and scales within an image. Therefore, detection of faces requires high computation cost which prevents from being used in real time applications. In this paper, we present face detection approach by using multiple classifiers for reducing the search space and improving detection accuracy. We design three face classifiers which take different feature representation of local image: gradient, texture, and pixel intensity features. The designed three face classifiers are trained by error back propagation algorithm. The computational efficiency is achieved by coarse-to-fine classification approach. 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 where the window is finely scanned. In fine classification, the output of each face classifier is combined and then used for a reliable judgment on the existence of face. Experimental results demonstrate that our proposed method can significantly reduce the number of scans compared to the exhaustive full scanning technique and provides the high detection rate.