Face detection in low-resolution images

Authors:
Shinji Hayashi;Osamu Hasegawa
Affiliations:
Tokyo Institute of Technology, Yokohama, Japan;Tokyo Institute of Technology, Yokohama, Japan
Venue:
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
Year:
2005

Citing 6
Cited 0

Neural Network-Based Face Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey

IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Learning of Multi-view Face Detection

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Feature-centric evaluation for efficient cascaded object detection

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Fast rotation invariant multi-view face detection based on real adaboost

FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Kullback-leibler boosting

CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

Face detection is a hot research topic in Computer Vision; the field has greatly progressed over the past decade. However face detection in low-resolution images has not been studied. In this paper, we use a conventional AdaBoost-based face detector to show that the face detection rate falls to 39% from 88% as face resolution decreases from 24 × 24 pixels to 6 × 6 pixels. We propose a new face detection method comprising four techniques for low-resolution images. As a result, our method improved the face detection rate from 39% to 71% for 6 × 6 pixel faces of MIT+CMU frontal face test set.