Towards an automatic human face localization system
BMVC '95 Proceedings of the 6th British conference on Machine vision (Vol. 2)
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face detection using local maxima
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Convolutional Face Finder: A Neural Architecture for Fast and Robust Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Faster and Better: A Machine Learning Approach to Corner Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
The BANCA database and evaluation protocol
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Feature-centric evaluation for efficient cascaded object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Face detection with the modified census transform
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Online domain adaptation of a pre-trained cascade of classifiers
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Hi-index | 0.00 |
The sliding window approach is the most widely used technique to detect an object from an image. In the past few years, classifiers have been improved in many ways to increase the scanning speed. Apart from the classifier design (such as the cascade), the scanning speed also depends on a number of different factors (such as the grid spacing, and the scale at which the image is searched). When the scanning grid spacing is larger than the tolerance of the trained classifier it suffers from low detections. In this paper, we present a technique to reduce the number of missed detections when fewer subwindows are processed in the sliding window approach for face detection. This is achieved by using a small patch to predict the location of the face within a local search area. We use simple binary features and a decision tree for location estimation as it proved to be efficient for our application. We also show that by using a simple interest point detector based on quantized gradient orientation, as the front-end to the proposed location estimation technique, we can further improve the performance. Experimental evaluation on several face databases show better detection rate and speed with our proposed approach when fewer number of subwindows are processed compared to the standard scanning technique.