Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contextual Priming for Object Detection
International Journal of Computer Vision
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Machine Learning
A Pose-Invariant Descriptor for Human Detection and Segmentation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Robust Human Detection under Occlusion by Integrating Face and Person Detectors
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Optimal edge-based shape detection
IEEE Transactions on Image Processing
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Identifying humans under partial occlusion is a challenging problem in unconstrained scene understanding. In contrast to many existing works that model human appearance in isolation, we address this problem by studying the semantic context between human face and other body parts using Markov logic networks. By learning a set of probabilistic first-order logic rules that capture interactions between body parts under varying degrees of occlusion, and the relationship they share with the neighboring spatial windows, we obtain a graphical model representation of these instances to facilitate inference. We illustrate the efficacy of our method through experiments on standard human detection datasets, and an internally collected dataset with several occluding humans.