A Fast and Accurate Face Detector Based on Neural Networks
By Raphaël Féraud, Olivier Bernier, Jean-Emmanuel Viallet, Michel Collobert. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001
365 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/ebfab3c7d0fdffc3d2f570c105e7a4e43997d04c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Xiaogang Wang
- Andrew Zisserman
- Dacheng Tao
- Jitendra Malik
- Andrea Vedaldi
- Ming-Hsuan Yang
- Trevor Darrell
Citations in the corpus
- Robust object detection via soft cascade (2005, background): “F´eraud et al [1] pro-pose a four stage cascade that includes a motion filter, color filter, a neural network and a PCA-based classifier.”
- Recent advances in visual and infrared face recognition - a review (2005, background): “This category of methods includes neural network-based algorithms [44,45] that detect upright and frontal views of faces in gray-scale images.”
- Convolutional face finder: a neural architecture for fast and robust face detection (2004, methodology): “Low level analysis first deals with the segmentation of visual features using image properties such as edges [3], intensity [4], color [5], [6], motion [7], or generalized measures [8].”
- Discriminative Autoencoders for Small Targets Detection (2014, background): “[7] based on nonlinear manifold learning.”
- A Survey of Recent Advances in Face Detection (2010, methodology): “[16] proposed an approach based on a neural network model called the constrained generative model (CGM).”
- Neurovisual Control in the Quake II Environment (2012, background): “Their computational abilities for certain visual tasks have been shown to be much faster than conventional techniques [9], [10].”
- Respectful cameras: detecting visual markers in real-time to address privacy concerns (2007, methodology): “Approaches to object detection employ statistical classification methods including AdaBoost [51, 40], Neural Networks [17], and Support Vector Machines (SVMs) [41].”