The Experts below are selected from a list of 5571 Experts worldwide ranked by ideXlab platform
Prem Natarajan - One of the best experts on this subject based on the ideXlab platform.
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Facial Landmark Detection with Tweaked Convolutional Neural Networks
IEEE transactions on pattern analysis and machine intelligence, 2017Co-Authors: Tal Hassner, Kanggeon Kim, Gerard Medioni, Prem NatarajanAbstract:This paper concerns the problem of facial Landmark Detection. We provide a unique new analysis of the features produced at intermediate layers of a convolutional neural network (CNN) trained to regress facial Landmark coordinates. This analysis shows that while being processed by the CNN, face images can be partitioned in an unsupervised manner into subsets containing faces in similar poses (i.e., 3D views) and facial properties (e.g., presence or absence of eye-wear). Based on this finding, we describe a novel CNN architecture, specialized to regress the facial Landmark coordinates of faces in specific poses and appearances. To address the shortage of training data, particularly in extreme profile poses, we additionally present data augmentation techniques designed to provide sufficient training examples for each of these specialized sub-networks. The proposed Tweaked CNN (TCNN) architecture is shown to outperform existing Landmark Detection methods in an extensive battery of tests on the AFW, ALFW, and 300W benchmarks. Finally, to promote reproducibility of our results, we make code and trained models publicly available through our project webpage.
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Facial Landmark Detection with Tweaked Convolutional Neural Networks
arXiv: Computer Vision and Pattern Recognition, 2015Co-Authors: Tal Hassner, Kanggeon Kim, Gerard Medioni, Prem NatarajanAbstract:We present a novel convolutional neural network (CNN) design for facial Landmark coordinate regression. We examine the intermediate features of a standard CNN trained for Landmark Detection and show that features extracted from later, more specialized layers capture rough Landmark locations. This provides a natural means of applying differential treatment midway through the network, tweaking processing based on facial alignment. The resulting Tweaked CNN model (TCNN) harnesses the robustness of CNNs for Landmark Detection, in an appearance-sensitive manner without training multi-part or multi-scale models. Our results on standard face Landmark Detection and face verification benchmarks show TCNN to surpasses previously published performances by wide margins.
Jean-luc Dugelay - One of the best experts on this subject based on the ideXlab platform.
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Multi-spectral Facial Landmark Detection
2020 IEEE International Workshop on Information Forensics and Security (WIFS), 2020Co-Authors: Jin Keong, Xingbo Dong, Zhe Jin, Khawla Mallat, Jean-luc DugelayAbstract:Thermal face image analysis is favorable for certain circumstances. For example, illumination-sensitive applications, like nighttime surveillance; and privacy-preserving demanded access control. However, the inadequate study on thermal face image analysis calls for attention in responding to the industry requirements. Detecting facial Landmark points are important for many face analysis tasks, such as face recognition, 3D face reconstruction, and face expression recognition. In this paper, we propose a robust neural network enabled facial Landmark Detection, namely Deep Multi-Spectral Learning (DMSL). Briefly, DMSL consists of two sub-models, i.e. face boundary Detection, and Landmark coordinates Detection. Such an architecture demonstrates the capability of detecting the facial Landmarks on both visible and thermal images. Particularly, the proposed DMSL model is robust in facial Landmark Detection where the face is partially occluded, or facing different directions. The experiment conducted on Eurecom’s visible and thermal paired database shows the superior performance of DMSL over the state-of-the-art for thermal facial Landmark Detection. In addition to that, we have annotated a thermal face dataset with their respective facial Landmark for the purpose of experimentation.
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IJCB - Facial Landmark Detection on thermal data via fully annotated visible-to-thermal data synthesis
2020 IEEE International Joint Conference on Biometrics (IJCB), 2020Co-Authors: Khawla Mallat, Jean-luc DugelayAbstract:Thermal imaging has substantially evolved, during the recent years, to be established as a complement, or even occasionally as an alternative to conventional visible light imaging, particularly for face analysis applications. Facial Landmark Detection is a crucial prerequisite for facial image processing. Given the upswing of deep learning based approaches, the performance of facial Landmark Detection has been significantly improved. However, this uprise is merely limited to visible spectrum based face analysis tasks, as there are only few research works on facial Landmark Detection in thermal spectrum. This limitation is mainly due to the lack of available thermal face databases provided with full facial Landmark annotations. In this paper, we propose to tackle this data shortage by converting existing face databases, designed for facial Landmark Detection task, from visible to thermal spectrum that will share the same provided facial Landmark annotations. Using the synthesized thermal databases along with the facial Landmark annotations, two different models are trained using active appearance models and deep alignment network. Evaluating the models trained on synthesized thermal data on real thermal data, we obtained facial Landmark Detection accuracy of 94.59% when tested on low quality thermal data and 95.63% when tested on high quality thermal data with a Detection threshold of 0.15×IOD.
Tal Hassner - One of the best experts on this subject based on the ideXlab platform.
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Facial Landmark Detection with Tweaked Convolutional Neural Networks
IEEE transactions on pattern analysis and machine intelligence, 2017Co-Authors: Tal Hassner, Kanggeon Kim, Gerard Medioni, Prem NatarajanAbstract:This paper concerns the problem of facial Landmark Detection. We provide a unique new analysis of the features produced at intermediate layers of a convolutional neural network (CNN) trained to regress facial Landmark coordinates. This analysis shows that while being processed by the CNN, face images can be partitioned in an unsupervised manner into subsets containing faces in similar poses (i.e., 3D views) and facial properties (e.g., presence or absence of eye-wear). Based on this finding, we describe a novel CNN architecture, specialized to regress the facial Landmark coordinates of faces in specific poses and appearances. To address the shortage of training data, particularly in extreme profile poses, we additionally present data augmentation techniques designed to provide sufficient training examples for each of these specialized sub-networks. The proposed Tweaked CNN (TCNN) architecture is shown to outperform existing Landmark Detection methods in an extensive battery of tests on the AFW, ALFW, and 300W benchmarks. Finally, to promote reproducibility of our results, we make code and trained models publicly available through our project webpage.
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Facial Landmark Detection with Tweaked Convolutional Neural Networks
arXiv: Computer Vision and Pattern Recognition, 2015Co-Authors: Tal Hassner, Kanggeon Kim, Gerard Medioni, Prem NatarajanAbstract:We present a novel convolutional neural network (CNN) design for facial Landmark coordinate regression. We examine the intermediate features of a standard CNN trained for Landmark Detection and show that features extracted from later, more specialized layers capture rough Landmark locations. This provides a natural means of applying differential treatment midway through the network, tweaking processing based on facial alignment. The resulting Tweaked CNN model (TCNN) harnesses the robustness of CNNs for Landmark Detection, in an appearance-sensitive manner without training multi-part or multi-scale models. Our results on standard face Landmark Detection and face verification benchmarks show TCNN to surpasses previously published performances by wide margins.
Xilin Chen - One of the best experts on this subject based on the ideXlab platform.
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robust fec cnn a high accuracy facial Landmark Detection system
Computer Vision and Pattern Recognition, 2017Co-Authors: Jie Zhang, Meina Kan, Shiguang Shan, Xilin ChenAbstract:Facial Landmark Detection, as a typical and crucial task in computer vision, is widely used in face recognition, face animation, facial expression analysis, etc. In the past decades, many efforts are devoted to designing robust facial Landmark Detection algorithms. However, it remains a challenging task due to extreme poses, exaggerated facial expression, unconstrained illumination, etc. In this work, we propose an effective facial Landmark Detection system, recorded as Robust FEC-CNN (RFC), which achieves impressive results on facial Landmark Detection in the wild. Considering the favorable ability of deep convolutional neural network, we resort to FEC-CNN as a basic method to characterize the complex nonlinearity from face appearance to shape. Moreover, face bounding box invariant technique is adopted to reduce the Landmark localization sensitivity to the face detector while model ensemble strategy is adopted to further enhance the Landmark localization performance. We participate the Menpo Facial Landmark Localisation in-the-Wild Challenge and our RFC significantly outperforms the baseline approach APS. Extensive experiments on Menpo Challenge dataset and IBUG dataset demonstrate the superior performance of the proposed RFC.
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CVPR Workshops - Robust FEC-CNN: A High Accuracy Facial Landmark Detection System
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017Co-Authors: Jie Zhang, Meina Kan, Shiguang Shan, Xilin ChenAbstract:Facial Landmark Detection, as a typical and crucial task in computer vision, is widely used in face recognition, face animation, facial expression analysis, etc. In the past decades, many efforts are devoted to designing robust facial Landmark Detection algorithms. However, it remains a challenging task due to extreme poses, exaggerated facial expression, unconstrained illumination, etc. In this work, we propose an effective facial Landmark Detection system, recorded as Robust FEC-CNN (RFC), which achieves impressive results on facial Landmark Detection in the wild. Considering the favorable ability of deep convolutional neural network, we resort to FEC-CNN as a basic method to characterize the complex nonlinearity from face appearance to shape. Moreover, face bounding box invariant technique is adopted to reduce the Landmark localization sensitivity to the face detector while model ensemble strategy is adopted to further enhance the Landmark localization performance. We participate the Menpo Facial Landmark Localisation in-the-Wild Challenge and our RFC significantly outperforms the baseline approach APS. Extensive experiments on Menpo Challenge dataset and IBUG dataset demonstrate the superior performance of the proposed RFC.
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FG - A Fully End-to-End Cascaded CNN for Facial Landmark Detection
2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), 2017Co-Authors: Meina Kan, Jie Zhang, Xilin Chen, Shiguang ShanAbstract:Facial Landmark Detection plays an important role in computer vision. It is a challenging problem due to various poses, exaggerated expressions and partial occlusions. In this work, we propose a Fully End-to-End Cascaded Convolutional Neural Network (FEC-CNN) for more promising facial Landmark Detection. Specifically, FEC-CNN includes several sub- CNNs, which progressively refine the shape prediction via finer and finer modeling, and the overall network is optimized fully end-to-end. Experiments on three challenging datasets, IBUG, 300W competition and AFLW, demonstrate that the proposed method is robust to large poses, exaggerated expressions and partial occlusions. The proposed FEC-CNN significantly improves the accuracy of Landmark prediction.
Khawla Mallat - One of the best experts on this subject based on the ideXlab platform.
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Multi-spectral Facial Landmark Detection
2020 IEEE International Workshop on Information Forensics and Security (WIFS), 2020Co-Authors: Jin Keong, Xingbo Dong, Zhe Jin, Khawla Mallat, Jean-luc DugelayAbstract:Thermal face image analysis is favorable for certain circumstances. For example, illumination-sensitive applications, like nighttime surveillance; and privacy-preserving demanded access control. However, the inadequate study on thermal face image analysis calls for attention in responding to the industry requirements. Detecting facial Landmark points are important for many face analysis tasks, such as face recognition, 3D face reconstruction, and face expression recognition. In this paper, we propose a robust neural network enabled facial Landmark Detection, namely Deep Multi-Spectral Learning (DMSL). Briefly, DMSL consists of two sub-models, i.e. face boundary Detection, and Landmark coordinates Detection. Such an architecture demonstrates the capability of detecting the facial Landmarks on both visible and thermal images. Particularly, the proposed DMSL model is robust in facial Landmark Detection where the face is partially occluded, or facing different directions. The experiment conducted on Eurecom’s visible and thermal paired database shows the superior performance of DMSL over the state-of-the-art for thermal facial Landmark Detection. In addition to that, we have annotated a thermal face dataset with their respective facial Landmark for the purpose of experimentation.
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IJCB - Facial Landmark Detection on thermal data via fully annotated visible-to-thermal data synthesis
2020 IEEE International Joint Conference on Biometrics (IJCB), 2020Co-Authors: Khawla Mallat, Jean-luc DugelayAbstract:Thermal imaging has substantially evolved, during the recent years, to be established as a complement, or even occasionally as an alternative to conventional visible light imaging, particularly for face analysis applications. Facial Landmark Detection is a crucial prerequisite for facial image processing. Given the upswing of deep learning based approaches, the performance of facial Landmark Detection has been significantly improved. However, this uprise is merely limited to visible spectrum based face analysis tasks, as there are only few research works on facial Landmark Detection in thermal spectrum. This limitation is mainly due to the lack of available thermal face databases provided with full facial Landmark annotations. In this paper, we propose to tackle this data shortage by converting existing face databases, designed for facial Landmark Detection task, from visible to thermal spectrum that will share the same provided facial Landmark annotations. Using the synthesized thermal databases along with the facial Landmark annotations, two different models are trained using active appearance models and deep alignment network. Evaluating the models trained on synthesized thermal data on real thermal data, we obtained facial Landmark Detection accuracy of 94.59% when tested on low quality thermal data and 95.63% when tested on high quality thermal data with a Detection threshold of 0.15×IOD.
