FloatBoost learning and statistical face detection

By S. Li, ZhenQiu Zhang. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004

592 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/bc890611cb8c94024c8086b82716f4a90e272e42

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Chained Cascade Network for Object Detection (2017, methodology): “It has been widely used in object detection [7, 2, 5, 17].”
Illumination Invariant Face Recognition Using Near-Infrared Images (2007, methodology): “In this paper, AdaBoost classifiers [50], with the extended Haar features proposed in [51], are used for face and eye detection.”
Hello! My name is... Buffy'' -- Automatic Naming of Characters in TV Video (2006, background): “The use of a frontal face detector restricts the video content we can label to frontal faces, but typically gives much greater reliability of detection than is currently obtainable using multi-view face detection [10].”
Scale-Aware Face Detection (2017, background): “Many works have been proposed to improve the Viola-Jones framework and achieves further improvements, such as local features [41, 20, 36], boosting algorithms [40, 21, 11], cascade structure [2] and multi-pose [22, 17, 12].”
Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering (2005, background): “In [21, 16 ], viewing angles are descritized and each training image is clustered beforehand.”
Real-Time Compressive Tracking (2012, methodology): “This problem is alleviated by boosting algorithms for selecting important features [20, 21].”
Learning Chained Deep Features and Classifiers for Cascade in Object Detection (2017, methodology): “Cascade can be applied for SVM [19, 8], boosted classi-ﬁers [6, 17, 31], and ConvNets [33].”
Robust object detection via soft cascade (2005, background): “(Note that both [3] and [6] report results based on a reduced set with 5 images containing hand-drawn faces removed.)”
Deep Learning for Generic Object Detection: A Survey (2018, unknown): “Chained Cascade Network と Cascade RCNN:カスケード [73, 20, 159] の本質は,多段階の分類器を使用し,後期段階がより難しい例の処理に集中できるよう早い段階で多数の簡単な負例を廃棄することで,識別性の高い分類器を学習することである.”
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, unknown): “From these couplings accurate relative solute geometries and information about solute conformational change can be obtained.”
Joint Haar-like features for face detection (2005, methodology): “There are modified versions of AdaBoost such as RealBoost [8], KLBoosting [9] and FloatBoost [10].”