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
Pattern Recognition Letters
Frontal-view face detection and facial features extraction using color and morphological operations
Pattern Recognition Letters
Face detection for image annotation
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
A new face detection method based on shape information
Pattern Recognition Letters
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Detection and Gesture Recognition for Human-Computer Interaction
Face Detection and Gesture Recognition for Human-Computer Interaction
Image prediction using face detection and triangulation
Pattern Recognition Letters
Face Detection With Information-Based Maximum Discrimination
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Rotation Invariant Neural Network-Based Face Detection
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Rule-based face detection in frontal views
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
Finding faces in cluttered scenes using random labeled graph matching
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
FloatBoost Learning and Statistical Face Detection
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
A multi-expert approach for robust face detection
Pattern Recognition
Face detection using discriminating feature analysis and Support Vector Machine
Pattern Recognition
Face detection in gray scale images using locally linear embeddings
Computer Vision and Image Understanding
Multi-view face and eye detection using discriminant features
Computer Vision and Image Understanding
Face detection and facial feature localization without considering the appearance of image context
Image and Vision Computing
ENCARA2: Real-time detection of multiple faces at different resolutions in video streams
Journal of Visual Communication and Image Representation
Face detection with boosted Gaussian features
Pattern Recognition
Face detection and tracking in video sequences using the modifiedcensus transformation
Image and Vision Computing
A hierarchical neural network for human face detection
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
A Bayesian discriminating features method for face detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The rapid and successful detection of a face in an image is a prerequisite to a fully automated face recognition system. A new neural network-based face detection system is presented, which is the outcome of a comparative study of two neural network models of different architecture and complexity. The fundamental difference in the construction of the two models is the need to address the problem either by using a general solution based on the full-face image or by composing the solution through the resolution of specific characteristics of the face. The algorithm is based on the assumption that there exists contrast in brightness between specific regions of the human face. The proposed neural network system is reliable and of reduced error rate. Specifically, we show that the second approach, even though more complicated, exhibits better performance in terms of detection and false - positive rates. Moreover, it can detect successfully faces that are slightly rotated out of the image plane.