Fast rotation invariant multi-view face detection based on real Adaboost

By Bo Wu, Haizhou Ai, Chang Huang, Shihong Lao. Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings., 2004

405 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/73d8fbafae3b09568ec63e8683b563d395f48308

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In Computer Vision and Pattern Recognition

Citations in the corpus

Joint Cascade Face Detection and Alignment (2014, background): “Many works are on multi-view face detection [10,17,27,7].”
On-line Boosting and Vision (2006, neutral): “[138] for fast rotation invariant face detection.”
SAN: Learning Relationship between Convolutional Features for Multi-Scale Object Detection (2018, background): “A typical way of reducing the variance is partitioning samples by pose [18,20,34], rotation [36] or resolution [25,35].”
Joint Haar-like features for face detection (2005, background): “As shown in Figure 1, in addition to the basic feature set (a), different arrangements or numbers of rectangles such as (b), (c) or (d) are used in [7, 11].”
A survey on face detection in the wild: Past, present and future (2015, methodology): “RepresentativeWorks General AdaBoost[17] boosting RealBoost[59] [60] [61] [62] schemes GentleBoost[107] [66] FloatBoost[59] Reuseprevious Boostingchain[110] nodes'results Nestedcascade[61] Introduce AsymmetricBoosting[111] [112] [113] asymmetry Linearasymmetricclassifier[116] SetintermediateFixednod”
Multi-view Face Detection Using Deep Convolutional Neural Networks (2015, background): “For example, [41] proposed to train a detector cascade for each view of the face and combined their results at the test time.”
Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm (2005, neutral): “A u t h o r ' s p e r s o n a l”
A Survey of Recent Advances in Face Detection (2010, methodology): “Reuse previous Boosting chain [108] nodes’ results Nested cascade [101]”
High-Performance Rotation Invariant Multiview Face Detection (2007, methodology): “…for an input pattern in branching nodes with the nonexclusive-vector strong classifiers learned by the Vector Boosting algorithm as claimed in Section 2 and Section 3, a comparison is made among four approaches, including the WFS tree, the pyramid [9], the parallel cascades [13], and the AdaBoost.”
Face Detection Based on Multi-Block LBP Representation (2007, result): “This function is similar to the lookup table (LUT) weak classifier for Haar-like features [1], the difference is that the LUT classifier gives a partition of real-value domain.”
Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applications (2016, methodology): “Some methods overcome these weaknesses by considering multiple posespecific detectors and either a pose router [61] or a probabilistic approach [178].”