S. Gong, S. Mckenna, A. Psarrou, Dynamic Vision: From Images to Face Recognition, Imperial College Press, 2000. Home/Search Document Not in Database Summary Related Articles Check |
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Component-based Face Recognition with 3D Morphable Models - Huang (2003) (3 citations) (Correct)
....detector. However, the complexity of the system increases with the multiple detection tasks which need to be performed. Component based methods are mainly characterized by the use of a priori knowledge to represent the face in smaller parts and its constraints on the spatial configuration of parts [16]. Recently, component based approaches have shown promising results in various object detection and recognition tasks such as face detection [17, 9] person detection [11] and face recognition [4, 23, 12, 8] The system in [9] was a SVM based recognition system which decomposed the face into a ....
S. McKenna S. Gong and A. Psarrou. Dynamic Vision: From Images to Face Recognition. Imperial College Press, Ireland, 2000.
Robust Parameterized Component Analysis: Theory and.. - Torre, Black (2002) (3 citations) (Correct)
....accuracy. We illustrate the use of the 2D PSFAM model with several applications including video conferencing, realistic avatar animation and eye tracking. 1 Introduction Many computer vision researchers have used Principal Component Analysis (PCA) to parameterize appearance, shape or motion [3,10,23,30]. However, one major drawback of this traditional technique is that it needs normalized samples in the training data. In the case of computer vision applications, the result is that the samples have to be aligned or geometrically normalized (we assume that other normalizations, e.g. photometric, ....
....samples in the training data. In the case of computer vision applications, the result is that the samples have to be aligned or geometrically normalized (we assume that other normalizations, e.g. photometric, have already been done) Previous methods for constructing appearance or shape models [10, 16,17,23,30,31] have cropped the region of interest by hand, or have used a hand labeled pre defined feature points to apply the translation, scaling and rotation that brought each image into alignment with a prototype. These manual approaches are likely to introduce errors into the model due to inaccuracies ....
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S. Gong, S. Mckenna, and A. Psarrou. Dynamic Vision: From Images to Face Recognition. Imperial College Press, 2000.
Direct Appearance Models - Hou, Li, Zhang, Cheng (13 citations) (Correct)
....important information useful for characterizing the face appearance [1] Alignment of a given face to a canonical face enables extraction of refined shape and texture parameters in the coordinate system of the canonical face model. It is crucial for high accuracy face recognition and synthesis [13, 8, 9, 3, 11]. The active appearance model (AAM) 5] is a powerful model for face alignment, recognition [9] and synthesis [3] It makes ingenious use of subspace analysis techniques, PCA in particular, to model both shape variation and texture variation, and the correlations between them. The idea is to warp ....
S. Gong, S. McKenna, and A. Psarrou. Dynamic Vision: From Images to Face Recognition. World Scientific Publishing and Imperial College Press, April 2000.
Modelling Faces Dynamically Across Views and Over Time - Yongmin Li Shaogang (2001) (6 citations) Self-citation (Gong) (Correct)
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S. Gong, S. McKenna, and A. Psarrou. Dynamic Vision: From Images to Face Recognition. World Scientific Publishing and Imperial College Press, April 2000.
Support vector machine based multi-view face detection and.. - Shaogang Gong Jamie (2004) Self-citation (Gong) (Correct)
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S. Gong, S. McKenna, A. Psarrou, Dynamic Vision: From Images to Face Recognition, World Scientific Publishing and Imperial College Press, 2000.
Recognising Trajectories of Facial Identities Using Kernel.. - Li, Gong, Liddell (2003) Self-citation (Gong) (Correct)
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S. Gong, S. McKenna, A. Psarrou, Dynamic Vision: From Images to Face Recognition, World Scientific Publishing/Imperial College Press, 2000.
Constructing Facial Identity Surfaces in a Nonlinear.. - Yongmin Li Shaogang (2001) (3 citations) Self-citation (Gong) (Correct)
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S. Gong, S. McKenna, and A. Psarrou. Dynamic Vision: From Images to Face Recognition. World Scientific Publishing and Imperial College Press, April 2000.
Constructing Structures of Facial Identities on the View.. - Li, Gong, Liddell (2001) Self-citation (Gong) (Correct)
....a multi view dynamic face model onto multi view face images and warping them to the model mean shape in frontal view. The model is trained on a set of multi view face images taken from 12 subjects, 45 views of each sub ject ( 20 , 20 ] in tilt and [ 40, 40] in yaw with an interval of 10 ) [13]. Each image in the training set is associated with known pose in tilt and yaw and labelled semi automatically with a set of salient landmarks including the positions of eyes, nose, mouth and face contour. More details of the model are discussed in [18] Figure 2 shows the original face images, ....
S. Gong, S. McKenna, and A. Psarrou. Dynamic Vision: From Images to Face Recognition. World Scientific Publishing and Imperial College Press, April 2000.
Modelling Faces Dynamically Across Views and Over Time - Yongmin Li Shaogang (2001) (6 citations) Self-citation (Gong) (Correct)
....the face dynamics using spatial temporal information from video sequences has also received great interest. From video sequences, not only can more information about the visual objects be acquired, but also the temporal continuity and subject constancy can provide a more robust representation [8]. Gong et al. 9] introduced an approach that uses Partially Recurrent Neural Networks to recognise temporal signatures of faces. Edwards et al. 6] proposed an integrated approach to decouple the identity variance from the residual variance of pose, lighting and expression. By learning the ....
....the dense 3D data. In this work, we learn a 3D face shape model containing only a sparse set of feature points from 2D face images in different views. 2.1.1. Database of 2D Multi view Faces The database used in this work includes 2D face images from 31 subjects, 133 poses of each subject (see [8] for more details of the data acquisition process) The pose of a face is defined by two parameters: tilt and yaw , the rotation angles about horizontal and vertical axes respectively. The rotation in image plane is not taken into account on the basis that human heads are assumed mostly ....
S. Gong, S. McKenna, and A. Psarrou. Dynamic Vision: From Images to Face Recognition. World Scientific Publishing and Imperial College Press, April 2000.
Video-Based Online Face Recognition Using Identity Surfaces - Yongmin Li Shaogang (2001) (1 citation) Self-citation (Gong) (Correct)
....difficult, e.g. by thresholding the images or showing them in blurred or pixellated formats. In a computer vision system, when faces are tracked consecutively, not only more information of those faces from different views but also the spatio temporal connection between those faces can be obtained [7]. Yamaguchi et al. 20] presented a method for face recognition from sequences by building a subspace for the detected faces on the given sequence and then matching the subspace with prototype subspaces. Gong et al. 8] introduced an approach that uses Partially Recurrent Neural Networks (PRNNs) ....
S. Gong, S. McKenna, and A. Psarrou. Dynamic Vision: From Images to Face Recognition. World Scientific Publishing and Imperial College Press, April 2000.
Recognising Trajectories of Facial Identities Using Kernel.. - Li, Gong, Liddell (2001) Self-citation (Gong) (Correct)
.... human vision system s ability to recognise animated faces is better than that on randomly ordered still face images (i.e. the same set of images, but displayed in random order without the temporal context of moving faces) 8, 1] For computer vision systems, although some work has been reported [7, 4, 6], the problem of recognising the dynamics of human faces in a spatio temporal context remains largely unresolved. In this work, we present a comprehensive approach to address the three challenging problems in face recognition stated above. A multi view dynamic face model is designed to extract ....
S. Gong, S. McKenna, and A. Psarrou. Dynamic Vision: From Images to Face Recognition. World Scientific Publishing and Imperial College Press, April 2000.
Modelling Faces Dynamically Across Views and Over Time - Li, Gong, Liddell (2001) (6 citations) Self-citation (Gong) (Correct)
....the face dynamics using spatial temporal information from video sequences has also received great interest. From video sequences, not only can more information about the visual objects be acquired, but also the temporal continuity and subject constancy can provide a more robust representation [8]. Gong et al. 9] introduced an approach that uses Partially Recurrent Neural Networks to recognise temporal signatures of faces. Edwards et al. 6] proposed an integrated approach to decouple the identity variance from the residual variance of pose, lighting and expression. By learning the ....
....the dense 3D data. In this work, we learn a 3D face shape model containing only a sparse set of feature points from 2D face images in different views. 2.1.1. Database of 2D Multi view Faces The database used in this work includes 2D face images from 31 subjects, 133 poses of each subject (see [8] for more details of the data acquisition process) The pose of a face is defined by two parameters: tilt and yaw ( the rotation angles about horizontal and vertical axes respectively. The rotation in image plane is not taken into account on the basis that human heads are assumed mostly ....
S. Gong, S. McKenna, and A. Psarrou. Dynamic Vision: From Images to Face Recognition. World Scientific Publishing and Imperial College Press, April 2000.
Recognising the Dynamics of Faces across Multiple Views - Li, Gong, Liddell (2000) Self-citation (Gong) (Correct)
....In many real world applications, a sequence of images containing the subjects to be recognised are necessarily acquired. With a sequence which records a face varying continuously over time and across views, not only more information can be obtained, but also the dynamics of faces can be captured [12, 1]. Yamaguchi et al. 28] presented a method for face recognition from sequences by building a subspace for the detected faces on the given sequence and then matching the subspace with prototype subspaces. Gong et al. 11] introduced an approach that uses Partially Recurrent Neural Networks (PRNNs) ....
S. Gong and A. P. S. McKenna. Dynamic Vision: From Images to Face Recognition. World Scientific Publishing and Imperial College Press, April 2000.
Robust Parameterized Component - Analysis Theory And (2002) (Correct)
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S. Gong, S. Mckenna, A. Psarrou, Dynamic Vision: From Images to Face Recognition, Imperial College Press, 2000.
Recognition of Humans and Their Activities Using Video - Chellappa, Roy-Chowdhury.. (Correct)
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S. Gong, S. McKenna, and A. Psarrou. Dynamic Vision: From Images to Face Recognition. Singapore: World Scientific, 2000.
A Relaxation Algorithm for Real-time Multiple View 3D-Tracking - Li, Hilton, Illingworth (Correct)
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Gong, S., McKenna, S.J. and Psarrou, A. Dynamic Vision: From Images to Face Recognition, Imperial College Press, London, 2000.
Robust Parameterized Component Analysis: Theory and.. - Torre, Black (2003) (3 citations) (Correct)
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S. Gong, S. Mckenna, A. Psarrou, Dynamic Vision: From Images to Face Recognition, Imperial College Press, 2000.
Classifying Surveillance Events from Attributes and Behaviour - Remagnino, Jones (2001) (Correct)
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S. Gong, S. McKenna, and A. Psarrou. "Dynamic Vision: From Images to Face Recognition". Imperial College Press, 2000.
Detection of Human Faces in Compressed Domain for Video.. - Chua, Zhao, Kankanhalli (2002) (Correct)
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Gong S, McKenna SJ, Psarrou A (2000) Dynamic Vision: From Images to Face Recognition, Imperial College Press, London
Real-Time Facial Feature Tracking Using An Active Model With Fast .. - Ahlberg (Correct)
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S. Gong, S.J. McKenna, A. Psarrou, Dynamic Vision -- From Images to Face Recognition, Imperial College Press, London, 2000.
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