Machine Learning - Special issue on inductive transfer
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
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
Statistical Learning of Multi-view Face Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Rotation Invariant Neural Network-Based Face Detection
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Support Vector Regression and Classification Based Multi-View Face Detection and Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Learning methods for generic object recognition with invariance to pose and lighting
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Identifying histological elements with convolutional neural networks
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
WSEAS Transactions on Signal Processing
ACM Transactions on Accessible Computing (TACCESS)
Deep learning from temporal coherence in video
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Facial pose estimation using a symmetrical feature model
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
A new representation method of head images for head pose estimation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
3D head pose estimation and tracking using particle filtering and ICP algorithm
AMDO'10 Proceedings of the 6th international conference on Articulated motion and deformable objects
Accelerating large-scale convolutional neural networks with parallel graphics multiprocessors
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Evaluation of pooling operations in convolutional architectures for object recognition
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Research frontier: deep machine learning--a new frontier in artificial intelligence research
IEEE Computational Intelligence Magazine
From engineering diagrams to engineering models: Visual recognition and applications
Computer-Aided Design
Auto-alignment of knee MR scout scans through redundant, adaptive and hierarchical anatomy detection
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Multi-scale convolutional neural networks for natural scene license plate detection
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Depth-based real time head pose tracking using 3D template matching
SIGGRAPH Asia 2012 Technical Briefs
3D aided face recognition across pose variations
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
A convolutional neural network for pedestrian gender recognition
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Combining texture and stereo disparity cues for real-time face detection
Image Communication
Robotics and Autonomous Systems
Detecting People Looking at Each Other in Videos
International Journal of Computer Vision
Visual Focus of Attention in Non-calibrated Environments using Gaze Estimation
International Journal of Computer Vision
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We describe a novel method for simultaneously detecting faces and estimating their pose in real time. The method employs a convolutional network to map images of faces to points on a low-dimensional manifold parametrized by pose, and images of non-faces to points far away from that manifold. Given an image, detecting a face and estimating its pose is viewed as minimizing an energy function with respect to the face/non-face binary variable and the continuous pose parameters. The system is trained to minimize a loss function that drives correct combinations of labels and pose to be associated with lower energy values than incorrect ones. The system is designed to handle very large range of poses without retraining. The performance of the system was tested on three standard data sets---for frontal views, rotated faces, and profiles---is comparable to previous systems that are designed to handle a single one of these data sets. We show that a system trained simuiltaneously for detection and pose estimation is more accurate on both tasks than similar systems trained for each task separately.