Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Independent component analysis: algorithms and applications
Neural Networks
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Investigation into Face Pose Distributions
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
A Fast and Accurate Face Detector for Indexation of Face Images
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
RATFG-RTS '99 Proceedings of the International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems
Finding faces in cluttered scenes using random labeled graph matching
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Face Pose Discrimination Using Support Vector Machines (SVM)
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Higher Order Statistical Learning for Vehicle Detection in Images
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Multi-View Face Pose Estimation Based on Supervised ISA Learning
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Face recognition: component-based versus global approaches
Computer Vision and Image Understanding - Special issue on Face recognition
FloatBoost Learning and Statistical Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convolutional Face Finder: A Neural Architecture for Fast and Robust Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Detect Objects in Images via a Sparse, Part-Based Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vector Boosting for Rotation Invariant Multi-View Face Detection
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Face detection and facial feature localization without considering the appearance of image context
Image and Vision Computing
Neural Computation
Face detection and tracking in video sequences using the modifiedcensus transformation
Image and Vision Computing
Feature-centric evaluation for efficient cascaded object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Learning object detection from a small number of examples: the importance of good features
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
Preceding vehicle recognition based on learning from sample images
IEEE Transactions on Intelligent Transportation Systems
Face detection using spectral histograms and SVMs
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Bayesian discriminating features method for face detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust multipose face detection in images
IEEE Transactions on Circuits and Systems for Video Technology
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
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This study develops a novel statistical system for automatic multi-view face detection and pose estimation. The five-module detection system is based on significant local facial features (or subregions) rather than the entire face. The low- and high-frequency feature information of each subregion of the facial image are extracted and projected onto the eigenspace and residual independent basis space in order to create the corresponding PCA (principal component analysis) projection weight vector and ICA (independent component analysis) coefficient vector, respectively. Therefore, the proposed system has an improved tolerance toward different facial expressions, wide viewing angles, partial occlusions and lighting conditions. Furthermore, either projection weight vectors or coefficient vectors in the PCA or ICA space have divergent distributions and are therefore modeled by using the weighted Gaussian mixture model (GMM) rather than a single Gaussian model. The GMM weights and parameters of the GMM are estimated iteratively using the Expectation-Maximization (EM) algorithm. Face detection is then performed by conducting a likelihood evaluation process based on the estimated joint probability of the weight and coefficient vectors and the corresponding geometric positions of the subregions. The use of subregion position information can reduce the risk of false acceptances. Moreover, simple cascaded rejecter module is employed to exclude 85% of the non-face images in order to enhance the overall system performance. The computational overhead is further reduced by eliminating the requirement for a residual image reconstruction process in the ICA process. Finally, the performance of the proposed system is evaluated using challenging databases. The results not only demonstrate the ability of the system to automatically identify facial images with a high degree of accuracy, but also verify its ability to estimate the fine pose angles with 5^o precision and an over 90% accuracy rate.