Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
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Face Recognition by Elastic Bunch Graph Matching
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An equivalence between sparse approximation and support vector machines
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Making large-scale support vector machine learning practical
Advances in kernel methods
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
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Training Support Vector Machines: an Application to Face Detection
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An introduction to variable and feature selection
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Use of the zero norm with linear models and kernel methods
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Face recognition: A literature survey
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Robust Real-Time Face Detection
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FloatBoost Learning and Statistical Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Calculation of the Complete Optimal Classification Set
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Real-Time Face Detection Using Boosting in Hierarchical Feature Spaces
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Face Authentication Test on the BANCA Database
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Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Histogram feature-based Fisher linear discriminant for face detection
Neural Computing and Applications
A sparsity-enforcing method for learning face features
IEEE Transactions on Image Processing
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Learning personal specific facial dynamics for face recognition from videos
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Enhanced local texture feature sets for face recognition under difficult lighting conditions
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
A regularized approach to feature selection for face detection
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
IEEE Transactions on Neural Networks
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This paper proposes a general framework for selecting features in the computer vision domain--i.e., learning descriptions from data--where the prior knowledge related to the application is confined in the early stages. The main building block is a regularization algorithm based on a penalty term enforcing sparsity. The overall strategy we propose is also effective for training sets of limited size and reaches competitive performances with respect to the state-of-the-art. To show the versatility of the proposed strategy we apply it to both face detection and authentication, implementing two modules of a monitoring system working in real time in our lab. Aside from the choices of the feature dictionary and the training data, which require prior knowledge on the problem, the proposed method is fully automatic. The very good results obtained in different applications speak for the generality and the robustness of the framework.