Neural network-based face detection

By Henry A. Rowley, Shumeet Baluja, Takeo Kanade. 1996

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation (2013, methodology): “To adapt our CNN to the new task (detection) and the new domain (warped proposal windows), we continue stochastic gradient descent (SGD) training of the CNN parameters using only warped region proposals.”
Improving Neural Machine Translation Models with Monolingual Data (2015, methodology): “The production of synthetic parallel texts bears resemblance to data augmentation techniques used in computer vision, where datasets are often augmented with rotated, scaled, or otherwise distorted variants of the (limited) training set (Rowley et al., 1996).”
Training Region-Based Object Detectors with Online Hard Example Mining (2016, methodology): “The idea of dataset boot-strapping [25, 30], typically called hard negative mining in recent work [12], appears in the training of most successful object detectors [7, 9, 12, 13, 15, 16, 23, 25, 29].”
Dynamic Neural Networks: A Survey (2021, background): “Image Sa Object detection (face [40], [203], [204], facial point [205], pedestrian [206], general [33], [207], [208], [209], [210]) Image segmentation [107], [211], [212], Super resolution [213], Style transfer [214], Coarse-to-fine classification [215]”
On the Binding Problem in Artificial Neural Networks (2020, background): “A classifier can then be trained to determine which window contains an object (Rowley et al., 1998; Viola and Jones, 2001; Harzallah et al., 2009).”
Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors (2018, positive): “Error reduction approaches incorporate the select data based on the predicted reduction in classiﬁer error based on information (MacKay, 1992a), Monte Carlo estimation (Roy and McCallum, 2001), or hard-negative example mining (Sung, 1994; Rowley et al., 1998).”
A ConvNet for the 2020s (2022, background): “For many decades, this has been the default use of ConvNets, generally on limited object categories such as digits [43], faces [58, 76] and pedestrians [19, 63].”
CRAFT Objects from Images (2016, background): “In case of detecting one particular category of near rigid objects (like faces or pedestrians) with fixed aspect ratio, sliding window mechanism is often used [23, 28, 35].”
DenseBox: Unifying Landmark Localization with End to End Object Detection (2015, background): “Rowley et al.[31] train neural network-based detectors which only is activated on faces with speciﬁc size, and apply detectors on the image pyramid with sliding-window fashion.”
A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction (2014, methodology): “Neural networks have been effectively used in a variety of prediction algorithms, including speech and facial recognition as well as the aforementioned usage in protein secondary structure prediction [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [24], [33], [34], [35], [36]”
Supervision via competition: Robot adversaries for learning tasks (2016, background): “In fact, it is known from the rich history of ML that “all data is not equal” [5], [6].”
Gradient-based learning applied to document recognition (1998, background): “Other authors have used Neural Networks, or other clas-si(cid:12)ers such as Support Vector Machines for face detection with great success [96], [97].”
Towards General Purpose Vision Systems: An End-to-End Task-Agnostic Vision-Language Architecture (2021, background): “Since machine learning revolutionized face recognition [53] and face detection [48, 51], the computer vision field has produced fantastic special purpose learning-based systems for everything from image synthesis to human pose estimation.”
Three-dimensional image processing methods to identify and characterise aggregates in compacted asphalt mixtures (2010, methodology): “In the field of computer science, ANN has been extensively utilised for pattern recognition in images, with special emphasis on the application of face detection (Propp and Samal 1992, Rowley et al. 1998, Sung and Poggio 1998).”
Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection (2003, methodology): “All experiments were performed on the complete CMU Frontal Face Test Set of 130 grayscale pictures with 510 frontal faces [7].”
A survey on face detection in the wild: Past, present and future (2015, unknown): “Earlyrepresenta- tivemethodsincludedthedetectorsin[142] [143].”