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FloatBoost Learning and Statistical Face Detection
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Convolutional Face Finder: A Neural Architecture for Fast and Robust Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
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A multi-expert approach for robust face detection
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Face detection using discriminating feature analysis and Support Vector Machine
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Face detection and facial feature localization without considering the appearance of image context
Image and Vision Computing
Portrait beautification: A fast and robust approach
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A user-centered approach to rushes summarisation via highlight-detected keyframes
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A Context-Driven Bayesian Classification Method for Eye Location
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Robust face detection using Gabor filter features
Pattern Recognition Letters
Use of nested K-means for robust head location in visual surveillance system
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Robotic navigation in crowded environments: key challenges for autonomous navigation systems
PerMIS '08 Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems
Neural network based face detection from pre-scanned and row-column decomposed average face image
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
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ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Automatic summarization of rushes video using bipartite graphs
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CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
New colour SIFT descriptors for image classification with applications to biometrics
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Expert Systems with Applications: An International Journal
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FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Classification-based face detection using gabor filter features
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Face detection using one-class-based support vectors
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Classification-based face detection using compound features
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Detection and tracking faces in unconstrained color video streams
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A real-time vision-based framework for human-robot interaction
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Merging and arbitration strategy applied bayesian classification for eye location
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
A cascaded mixture SVM classifier for object detection
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Image context-driven eye location using the hybrid network of k-means and RBF
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Real time face detection system based edge restoration and nested k-means at frontal view
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
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Merging and arbitration strategy for robust eye location
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ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Object detection using context-based cascade classifier
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A boosting SVM chain learning for visual information retrieval
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FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
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Human face detection using skin color context awareness and context-based bayesian classifiers
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Mobile Multimedia Processing
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Computer Vision and Image Understanding
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Multimedia Tools and Applications
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
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This paper presents a novel Bayesian discriminating features (BDF) method for multiple frontal face detection. The BDF method, which is trained on images from only one database, yet works on test images from diverse sources, displays robust generalization performance. The novelty of this paper comes from the integration of the discriminating feature analysis of the input image, the statistical modeling of face and nonface classes, and the Bayes classifier for multiple frontal face detection. First, feature analysis derives a discriminating feature vector by combining the input image, its 1D Harr wavelet representation, and its amplitude projections. While the Harr wavelets produce an effective representation for object detection, the amplitude projections capture the vertical symmetric distributions and the horizontal characteristics of human face images. Second, statistical modeling estimates the conditional probability density functions, or PDFs, of the face and nonface classes, respectively. While the face class is usually modeled as a multivariate normal distribution, the nonface class is much more difficult to model due to the fact that it includes "the rest of the world." The estimation of such a broad category is, in practice, intractable. However, one can still derive a subset of the nonfaces that lie closest to the face class, and then model this particular subset as a multivariate normal distribution.