Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Boosting as entropy projection
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using the Discrete Cosine Transform
International Journal of Computer Vision - Special issue: Research at McGill University
JPEG Still Image Data Compression Standard
JPEG Still Image Data Compression Standard
Analysis of the Performance of AdaBoost.M2 for the Simulated Digit-Recognition-Example
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Boosting the margin: A new explanation for the effectiveness of voting methods
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Improving Algorithms for Boosting
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Face Recognition Using A DCT-HMM Approach
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Illumination ratio image: synthesizing and recognition with varying illuminations
Pattern Recognition Letters
Face recognition: component-based versus global approaches
Computer Vision and Image Understanding - Special issue on Face recognition
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Real-Time Face Detection
International Journal of Computer Vision
FloatBoost Learning and Statistical Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved-LDA based face recognition using both facial global and local information
Pattern Recognition Letters
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
MutualBoost learning for selecting Gabor features for face recognition
Pattern Recognition Letters
Shadow compensation in 2D images for face recognition
Pattern Recognition
Journal of Cognitive Neuroscience
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Illumination invariant face recognition
Pattern Recognition
Recognition of JPEG compressed face images based on AdaBoost
SAMT'07 Proceedings of the semantic and digital media technologies 2nd international conference on Semantic Multimedia
Face recognition using ada-boosted gabor features
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
A face and palmprint recognition approach based on discriminant DCT feature extraction
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
High-speed face recognition based on discrete cosine transform and RBF neural networks
IEEE Transactions on Neural Networks
Hi-index | 0.01 |
In this paper, we describe a novel multiclass boosting algorithm, EDBoost, to achieve robust face recognition directly in JPEG compressed domain. In comparison with existing boosting algorithms, the proposed EDBoost exploits Euclidean distance (ED) to eliminate non-effective weak classifiers in each iteration of the boosted learning, and hence improves both feature selection and classifier learning by using fewer weak classifiers and producing lower error rates. When applied to face recognition, the EDBoost algorithm is capable of selecting the most discriminative DCT features directly in JPEG compressed domain to achieve high recognition performances. In addition, a new DC replacement scheme is also proposed to reduce the effect of illumination changes. In comparison with the existing techniques, the proposed scheme achieves robust face recognition without losing the important information carried by all DC coefficients. Extensive experiments support the conclusion that the proposed algorithm outperforms all representative existing techniques in terms of boosted learning, multiclass classification, lighting effect reduction and face recognition rates.