The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
CAIP '97 Proceedings of the 7th International Conference on Computer Analysis of Images and Patterns
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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
Fast Human Detection Using a Cascade of Histograms of Oriented Gradients
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Journal of Cognitive Neuroscience
Face recognition using HOG-EBGM
Pattern Recognition Letters
Scale Space Histogram of Oriented Gradients for Human Detection
ISISE '08 Proceedings of the 2008 International Symposium on Information Science and Engieering - Volume 02
Co-occurrence Histograms of Oriented Gradients for Pedestrian Detection
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
AdaBoost learning for human detection based on histograms of oriented gradients
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Eigenface-based sparse representation for face recognition
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
A novel prototype generation technique for handwriting digit recognition
Pattern Recognition
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Face recognition has been a long standing problem in computer vision. Recently, Histograms of Oriented Gradients (HOGs) have proven to be an effective descriptor for object recognition in general and face recognition in particular. In this paper, we investigate a simple but powerful approach to make robust use of HOG features for face recognition. The three main contributions of this work are: First, in order to compensate for errors in facial feature detection due to occlusions, pose and illumination changes, we propose to extract HOG descriptors from a regular grid. Second, fusion of HOG descriptors at different scales allows to capture important structure for face recognition. Third, we identify the necessity of performing dimensionality reduction to remove noise and make the classification process less prone to overfitting. This is particularly important if HOG features are extracted from overlapping cells. Finally, experimental results on four databases illustrate the benefits of our approach.