LDR-LLE: LLE with Low-Dimensional Neighborhood Representation
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Local linear transformation embedding
Neurocomputing
Video retargeting with multi-scale trajectory optimization
Proceedings of the international conference on Multimedia information retrieval
Face detection through compact classifier using adaptive look-up-table
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Learning an intrinsic-variable preserving manifold for dynamic visual tracking
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Face transformation with harmonic models by the finite-volume method with delaunay triangulation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
International Journal of Multimedia Data Engineering & Management
Combining texture and stereo disparity cues for real-time face detection
Image Communication
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As a large-scale database of hundreds of thousands of face images collected from the Internet and digital cameras becomes available, how to utilize it to train a well-performed face detector is a quite challenging problem. In this paper, we propose a method to resample a representative training set from a collected large-scale database to train a robust human face detector. First, in a high-dimensional space, we estimate geodesic distances between pairs of face samples/examples inside the collected face set by isometric feature mapping (Isomap) and then subsample the face set. After that, we embed the face set to a low-dimensional manifold space and obtain the low-dimensional embedding. Subsequently, in the embedding, we interweave the face set based on the weights computed by locally linear embedding (LLE). Furthermore, we resample nonfaces by Isomap and LLE likewise. Using the resulting face and nonface samples, we train an AdaBoost-based face detector and run it on a large database to collect false alarms. We then use the false detections to train a one-class support vector machine (SVM). Combining the AdaBoost and one-class SVM-based face detector, we obtain a stronger detector. The experimental results on the MIT + CMU frontal face test set demonstrated that the proposed method significantly outperforms the other state-of-the-art methods.