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Face Alignment With Deep Regression
IEEE Transactions on Neural Networks and Learning Systems, 2018In this paper, we present a deep regression approach for face alignment. The deep regressor is a neural network that consists of a global layer and multistage local layers. The global layer estimates the initial face shape from the whole image, while the following local layers iteratively update the shape with local image observations.
Baoguang Shi +3 more
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009
This paper proposes a discriminative framework for efficiently aligning images. Although conventional Active Appearance Models (AAMs)-based approaches have achieved some success, they suffer from the generalization problem, i.e., how to align any image with a generic model.
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This paper proposes a discriminative framework for efficiently aligning images. Although conventional Active Appearance Models (AAMs)-based approaches have achieved some success, they suffer from the generalization problem, i.e., how to align any image with a generic model.
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Recurrent Convolutional Face Alignment
2017Mainstream direction in face alignment is now dominated by cascaded regression methods. These methods start from an image with an initial shape and build a set of shape increments by computing features with respect to the current shape estimate. These shape increments move the initial shape to the desired location.
Wang, Wei, Tulyakov, Sergey, Sebe, Nicu
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Improving alignment of faces for recognition
2011 IEEE International Symposium on Robotic and Sensors Environments (ROSE), 2011Face recognition systems for uncontrolled environments often work through an alignment, feature extraction, and recognition pipeline. Effective alignment of faces is thus crucial as can be an entry point in the process and poor alignments can greatly affect recognition performance.
Md. Kamrul Hasan 0004 +1 more
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Joint Face Alignment and 3D Face Reconstruction
2016We present an approach to simultaneously solve the two problems of face alignment and 3D face reconstruction from an input 2D face image of arbitrary poses and expressions. The proposed method iteratively and alternately applies two sets of cascaded regressors, one for updating 2D landmarks and the other for updating reconstructed pose-expression ...
Feng Liu 0013 +3 more
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Face alignment recurrent network
Pattern Recognition, 2018Abstract This paper presents a new facial landmark detection method for images and videos under uncontrolled conditions, based on a proposed Face Alignment Recurrent Network (FARN). The network works in recurrent fashion and is end-to-end trained to help avoid over-strong early stage regressors and over-weak later stage regressors as in many existing
Qiqi Hou +4 more
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Face alignment robust to occlusion
Face and Gesture 2011, 2011In this paper we present an approach to robustly align facial features to a face image even when the face is partially occluded. Previous methods are vulnerable to partial occlusion of the face, since it is assumed, explicitly or implicitly, that there is no significant occlusion.
Myung-Cheol Roh +2 more
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Face alignment by Explicit Shape Regression
2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012We present a very efficient, highly accurate, "Explicit Shape Regression" approach for face alignment. Unlike previous regression-based approaches, we directly learn a vectorial regression function to infer the whole facial shape (a set of facial landmarks) from the image and explicitly minimize the alignment errors over the training data. The inherent
Xudong Cao +3 more
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Joint Face Detection and Initialization for Face Alignment
2016This paper presents a joint face detection and initialization method for cascaded face alignment. Unlike existing methods which consider face detection and initialization as separate steps, we concurrently obtain a bounding box and initial facial landmarks (i.e. shape) in one step, yielding better accuracy and efficiency.
Zhiwei Wang 0002, Xin Yang 0008
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2011
In order to interpret images of faces (e.g., for recognition), it is important to have a model of the different ways that a face may appear. Though faces vary widely, changes can be broken down into two categories—changes in shape and changes in the texture (patterns of pixel values) across the face—that are largely due to differences between ...
Tresadern, P +3 more
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In order to interpret images of faces (e.g., for recognition), it is important to have a model of the different ways that a face may appear. Though faces vary widely, changes can be broken down into two categories—changes in shape and changes in the texture (patterns of pixel values) across the face—that are largely due to differences between ...
Tresadern, P +3 more
openaire +3 more sources