Towards automatic analysis of social interaction patterns in a nursing home environment from video
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Detecting social interactions of the elderly in a nursing home environment
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Multi-view face and eye detection using discriminant features
Computer Vision and Image Understanding
Expert system segmentation of face images
Expert Systems with Applications: An International Journal
A Regularized Framework for Feature Selection in Face Detection and Authentication
International Journal of Computer Vision
Face detection using simplified Gabor features and hierarchical regions in a cascade of classifiers
Pattern Recognition Letters
Haar-like features with optimally weighted rectangles for rapid object detection
Pattern Recognition
A sparsity-enforcing method for learning face features
IEEE Transactions on Image Processing
A theoretical approach to construct highly discriminative features with application in AdaBoost
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Framework for research on detection classifiers
Proceedings of the 24th Spring Conference on Computer Graphics
PCA enhanced training data for adaboost
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
A study of detecting social interaction with sensors in a nursing home environment
ICCV'05 Proceedings of the 2005 international conference on Computer Vision in Human-Computer Interaction
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Boosting-based methods have recently led to the state-of-the-art face detection systems. In these systems, weak classifiers to be boosted are based on simple, local, Haar-like features. However, it can be empirically observed that in later stages of the boosting process, the non-face examples collected by bootstrapping become very similar to the face examples, and the classification error of Haar-like feature-based weak classifiers is thus very close to 50%. As a result, the performance of a face detector cannot be further improved. This paper proposed a solution to this problem, introducing a face detection method based on boosting in hierarchical feature spaces (both local and global). We argue that global features, like those derived from Principal Component Analysis, can be advantageously used in the later stages of boosting, when local features do not provide any further benefit. We show that weak classifiers learned in hierarchical feature spaces are better boosted. Our methodology leads to a face detection system that achieves higher performance than a current state-of-the-art system, at a comparable speed.