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Optimization of a training set for more robust face detection

Jie Chen, Xilin Chen, Jie Yang, Shiguang Shan, Ruiping Wang, Wen Gao
2009 Pattern Recognition  
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

I. Visentini, C. Micheloni, G.L. Foresti
2007 2007 IEEE International Conference on Image Processing  
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

Koichiro Niinuma, Itir Onal Ertugrul, Jeffrey F. Cohn, László A. Jeni
2021 Frontiers in Computer Science  
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]

Avishek Joey Bose, Parham Aarabi
2018 arXiv   pre-print
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

Liting Wang, Xiaoqing Ding, Chi Fang
2008 2008 International Conference on Audio, Language and Image Processing  
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

Asraa Safaa Ahmed, Firas A. Abdullatif, Taha Mohammad Hasan
2019 International Journal on Advanced Science, Engineering and Information Technology  
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]

Yundong Zhang, Xiang Xu, Xiaotao Liu
2019 arXiv   pre-print
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

Li Zhang, Haizhou Ai, Shengjun Xin, Chang Huang, Shuichiro Tsukiji, Shihong Lao
2005 IEEE International Conference on Image Processing 2005  
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

Meng Yang, Zhizhao Feng, Simon C.K. Shiu, Lei Zhang
2014 Pattern Recognition  
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

Vincent Rapp, Kevin Bailly, Thibaud Senechal, Lionel Prevost
2013 Image and Vision Computing  
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

Riccardo Volpi, Vittorio Murino
2019 2019 IEEE/CVF International Conference on Computer Vision (ICCV)  
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

Rong Xiao, Long Zhu, Hong-Jiang Zhang
2003 Proceedings Ninth IEEE International Conference on Computer Vision  
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]

Markus Storer, Peter M. Roth, Martin Urschler, Horst Bischof, Josef A. Birchbauer
2010 Communications in Computer and Information Science  
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

Shervin Rahimzadeh Arashloo, Josef Kittler
2020 IEEE Transactions on Neural Networks and Learning Systems  
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

Shaolei Feng, S.K. Zhou, S. Good, D. Comaniciu
2009 2009 IEEE Conference on Computer Vision and Pattern Recognition  
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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