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Optimization of a training set for more robust face detection
2009
Pattern Recognition
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We propose a genetic algorithm (GA) and manifold-based method to resample a given training set for more robust face detection. ...
In this research, we study the methodology of automatically obtaining an optimal training set for robust face detection by resampling the collected training set. ...
Chen, et al., Optimization of a training set for more robust face detection, Pattern Recognition (2009), doi: 10.1016/j.patcog.2009.02.006 ...
doi:10.1016/j.patcog.2009.02.006
fatcat:lxhexvabezfapn545be7b2u2nu
Tuning Asymboost Cascades Improves Face Detection
2007
2007 IEEE International Conference on Image Processing
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The face detection problem is certainly one of the most studied topics in artificial vision. ...
Video surveillance and security systems, biometrics, HCI and multimedia applications are some examples of systems that exploit face localization to improve their robustness. ...
Acknowledgments This work was partially supported by the Italian Ministry of University and Scientific Research within the framework of the project "Ambient Intelligence: event analysis, sensor reconfiguration ...
doi:10.1109/icip.2007.4380058
dblp:conf/icip/VisentiniMF07
fatcat:wl6mupiahbbpvgdgdaatnjjocm
Systematic Evaluation of Design Choices for Deep Facial Action Coding Across Pose
2021
Frontiers in Computer Science
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The architecture achieved a 3.5% increase in F1 score for occurrence detection and a 5.8% increase in Intraclass Correlation (ICC) for intensity estimation. ...
in pre-training, feature alignment, model size selection, and optimizer details. ...
This research was supported in part by Fujitsu Laboratories of America, NIH awards NS100549 and MH096951, and NSF award CNS-1629716. ...
doi:10.3389/fcomp.2021.636094
fatcat:l2ze22edgvbx5mym5ls3426rai
Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimization
[article]
2018
arXiv
pre-print
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We evaluate our attack on a trained Faster R-CNN face detector on the cropped 300-W face dataset where we manage to reduce the number of detected faces to 0.5% of all originally detected faces. ...
only 0.5% of detected faces to a modest 5.0%. ...
While there are many possible choices for L misclassif y , such as the likelihood of the perturbed images under the face detector we find that certain objectives much more robust to the choice of a suitable ...
arXiv:1805.12302v1
fatcat:a4sy3gswbjb23kc6h55qrj5qkq
Generic face alignment using an improved Active Shape Model
2008
2008 International Conference on Audio, Language and Image Processing
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First, random forest classifiers are trained to recognize local appearance around each landmark. This discriminative learning provides more robustness weight for the optimization fitting procedure. ...
in appearance of many faces, including unseen faces not in the training set. ...
This discriminative learning provides more robustness weight for the parameter optimization procedure. Experimental results demonstrate its effectiveness on generic face alignment. ...
doi:10.1109/icalip.2008.4590037
fatcat:gwxrmwfspzc6xbfna2hbdufmzi
Generating and Validating DSA Private Keys from Online Face Images for Digital Signatures
2019
International Journal on Advanced Science, Engineering and Information Technology
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The use of the MTCNN for face detection and Facenet for face recognition, in addition to the proposed neural network, to achieved the best performance. ...
The proposed method uses a convolutional neural network that is trained using a semi-supervised approach, so that, the values used for the training are extracted based on the predictions of the neural ...
Output: Labels for individuals (L) Return L Using the calculated optimal value per each user, the neural network is trained for a set of epochs. ...
doi:10.18517/ijaseit.9.3.8950
fatcat:rbhfe3qzpralniqbhskaz47kmy
Robust and High Performance Face Detector
[article]
2019
arXiv
pre-print
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Over the most popular and challenging face detection benchmark, i.e., WIDER FACE, the proposed VIM-FD achieves state-of-the-art performance. ...
In recent years, face detection has experienced significant performance improvement with the boost of deep convolutional neural networks. ...
[26] propose to jointly train CascadeCNN to realize end-to-end optimization. Faceness [27] trains a series of CNNs for facial attribute recognition to detect partially occluded faces. ...
arXiv:1901.02350v1
fatcat:7kwo3sspifbfplw6pknxphiztu
Robust face alignment based on local texture classifiers
2005
IEEE International Conference on Image Processing 2005
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A Bayesian framework is configured for shape parameter optimization and the algorithm is implemented in a hierarchical structure for efficiency. ...
We propose a robust face alignment algorithm with a novel discriminative local texture model. ...
This kind of boosted classifier has been proven to be robust and efficient in face detection research. ...
doi:10.1109/icip.2005.1530065
dblp:conf/icip/ZhangAXHTL05
fatcat:2czw67wj7ffk5lyadxm57uqpfe
Fast and robust face recognition via coding residual map learning based adaptive masking
2014
Pattern Recognition
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Robust face recognition (FR) is an active topic in computer vision and biometrics, while face occlusion is one of the most challenging problems for robust FR. ...
A dictionary is learned to code the training samples, and the distribution of coding residuals is computed. Consequently, a residual map is learned to detect the occlusions by adaptive thresholding. ...
Intuitively, we can use the coding residual to detect the occluded pixels (for example, by setting a detection threshold), and then mask the detected occlusion pixels from the face coding to achieve robust ...
doi:10.1016/j.patcog.2013.08.003
fatcat:tn4oiurvnfh2xmqdr7n4fpg3tu
Multi-Kernel Appearance Model
2013
Image and Vision Computing
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To this end, parameters of a deformable shape model are optimized using the first step outputs through a Gauss-Newton algorithm. ...
of many applications dedicated to face analysis. ...
Digital Business cluster for digital content. ...
doi:10.1016/j.imavis.2013.04.006
fatcat:fjqxknr3pjd4xadwvqkyervlya
Addressing Model Vulnerability to Distributional Shifts Over Image Transformation Sets
2019
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
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We formulate a combinatorial optimization problem that allows evaluating the regions in the image space where a given model is more vulnerable, in terms of image transformations applied to the input, and ...
An empirical evaluation on classification and semantic segmentation problems suggests that the devised algorithm allows to train models that are more robust against content-preserving image manipulations ...
We are grateful to Jacopo Cavazza and Federico Marmoreo for helpful discussions concerning the problem formulation proposed in this work. ...
doi:10.1109/iccv.2019.00807
dblp:conf/iccv/VolpiM19
fatcat:swjwcyxeb5canlgjvs7dp3upuq
Boosting chain learning for object detection
2003
Proceedings Ninth IEEE International Conference on Computer Vision
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Experimental comparisons of boosting chain and boosting cascade are provided through a face detection problem. The promising results clearly demonstrate the effectiveness made by boosting chain. ...
Moreover, a linear optimization scheme is proposed to address the problems of redundancy in boosting learning and threshold adjusting in cascade coupling. ...
However, for a given detection task, the problem of finding the optimized set of f i and m i task is a major challenge for cascade classification. ...
doi:10.1109/iccv.2003.1238417
dblp:conf/iccv/XiaoZZ03
fatcat:dc6mx5j6kfbbbnd7g4qiyojsau
Efficient Robust Active Appearance Model Fitting
[chapter]
2010
Communications in Computer and Information Science
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Since existing methods for robust PCA reconstruction are computationally too expensive for real-time processing we applied a more efficient method: Fast-Robust PCA (FR-PCA). ...
Thus, if parts of the image are occluded the method converges to local minima and the obtained results are unreliable. To overcome this problem we propose a robust AAM fitting strategy. ...
Acknowledgements This work has been funded by the Biometrics Center of Siemens IT Solutions and Services, Siemens Austria. ...
doi:10.1007/978-3-642-11840-1_17
fatcat:cq4vqvj3gbdo7cqqzsundtcugm
Robust One-Class Kernel Spectral Regression
2020
IEEE Transactions on Neural Networks and Learning Systems
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Through extensive experiments, the proposed methodology is found to enhance robustness against contamination in the training set compared with the baseline kernel null-space method, as well as other existing ...
In this respect, first, the effect of the Tikhonov regularization in the Hilbert space is analyzed, where the one-class learning problem in the presence of contamination in the training set is posed as ...
For this purpose, the proposed models are trained on the face, MNIST, and Coil-100 data sets. ...
doi:10.1109/tnnls.2020.2979823
pmid:32481229
fatcat:wavzlyoat5fatlknhmynmouau4
Automatic fetal face detection from ultrasound volumes via learning 3D and 2D information
2009
2009 IEEE Conference on Computer Vision and Pattern Recognition
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However, manually searching for the optimal view of the fetal face in 3D ultrasound volumes is cumbersome and time-consuming even for expert physicians and sonographers. ...
Our approach applies a new technique -constrained marginal space learning -for 3D face mesh detection, and combines a boosting-based 2D profile detection to refine 3D face pose. ...
To infer the optimal profile shape for the detected location, a weighted nearest-neighbor approach by Georgescu et. al [5] is used to find the closest profile prototype in the training set. ...
doi:10.1109/cvprw.2009.5206527
fatcat:luayopcw4zb3jl4iz4loeos4j4
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