Convolutional face finder: a neural architecture for fast and robust face detection

By Christophe Garcia, Manolis Delakis. 2004

606 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

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Citations in the corpus

Large-Scale FPGA-based Convolutional Networks (2011, background): “…et al., 2003; Chellapilla et al., 2006), • detection in images, including faces with record accuracy and real-time performance (Vaillant et al., 1994; Garcia and Delakis, 2004; Osadchy et al., 2007; Nasse et al., 2009), license plates and faces in Google’s StreetView (Frome et al., 2009), or…”
Memory-centric accelerator design for Convolutional Neural Networks (2013, background): “Reported applications are face detection [5], traffic sign detection [6], and many others.”
A Survey of Recent Advances in Face Detection (2010, background): “Neural Constrained generative model [16] networks Convolutional neural network [24, 67]”
Multi-view Face Detection Using Deep Convolutional Neural Networks (2015, methodology): “[8] trained a single multi-layer network for face detection.”
Simplifying ConvNets for Fast Learning (2012, background): “[3, 5, 7] have shown that, for different object recognition tasks, state-of-the-art solutions can be achieved without non-linear activation functions in convolutional layers.”
Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering (2005, background): “There has been significant progress made recently in the area of face detection [21, 17, 16, 10, 6], most of which focuses on faces in frontal view.”
CNP: An FPGA-based processor for Convolutional Networks (2009, methodology): “The architecture of the network is quite similar to those described in [6, 7].”
text Detection with Convolutional Neural Networks (2008, neutral): “, 1998) and (Garcia and Delakis, 2004).”
Toward automatic phenotyping of developing embryos from videos (2005, background): “The present study primarily concerns the early stages of development, from fertilization to the four-cell stage.”
Convolutional networks and applications in vision (2010, methodology): “France Télécom has deployed ConvNetbased face detection systems for video-conference and other systems [15].”
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): “…S kl is employed, with S kl defined as averages over reorientational motion of the second-order Legendre polynomials: S kl ¼ 1 2 < 3 cos h Z;k cos h Z;l À d kl > ð2Þ where h Z,k is the angle between the molecule-fixed k-axis and the space-fixed Z-axis which lies along the liquid-crystal director.”
From Facial Parts Responses to Face Detection: A Deep Learning Approach (2015, methodology): “There is a long history of using neural network for the task of face detection [33, 26, 7, 25].”
Robust Object Detection with Interleaved Categorization and Segmentation (2008, background): “From this, it can be seen that the best recognition performance is achieved by the combinations of DoG+SIFT and DoG+Shape Context , (2004) and the one from Dalal and Triggs (2005).”
Dislocated Time Series Convolutional Neural Architecture: An Intelligent Fault Diagnosis Approach for Electric Machine (2017, methodology): “CNN has been used successfully in many different applications, such as handwriting recognition [25], [28], machineprinted character recognition [29], and face recognition [30], [31].”