Many current face recognition algorithms perform badly when the lighting or pose of the probe and... more Many current face recognition algorithms perform badly when the lighting or pose of the probe and gallery images differ. In this paper we present a novel algorithm designed for these conditions. We describe face data as resulting from a generative model which incorporates both withinindividual and between-individual variation. In recognition we calculate the likelihood that the differences between face images are entirely due to within-individual variability. We extend this to the non-linear case where an arbitrary face manifold can be described and noise is position-dependent. We also develop a "tied" version of the algorithm that allows explicit comparison across quite different viewing conditions. We demonstrate that our model produces state of the art results for (i) frontal face recognition (ii) face recognition under varying pose.
Proceedings Cvpr Ieee Computer Society Conference on Computer Vision and Pattern Recognition Ieee Computer Society Conference on Computer Vision and Pattern Recognition, 2009
Contemporary face recognition algorithms rely on precise localization of keypoints (corner of eye... more Contemporary face recognition algorithms rely on precise localization of keypoints (corner of eye, nose etc.). Unfortunately, finding keypoints reliably and accurately remains a hard problem. In this paper we pose two questions. First, is it possible to exploit the gallery image in order to find keypoints in the probe image? For instance, consider finding the left eye in the probe image. Rather than using a generic eye model, we use a model that is informed by the appearance of the eye in the gallery image. To this end we develop a probabilistic model which combines recognition and keypoint localization. Second, is it necessary to localize keypoints? Alternatively we can consider keypoint position as a hidden variable which we marginalize over in a Bayesian manner. We demonstrate that both of these innovations improve performance relative to conventional methods in both frontal and cross-pose face recognition.
Proceedings Cvpr Ieee Computer Society Conference on Computer Vision and Pattern Recognition Ieee Computer Society Conference on Computer Vision and Pattern Recognition, 2010
Unsupervised over-segmentation of an image into superpixels is a common preprocessing step for im... more Unsupervised over-segmentation of an image into superpixels is a common preprocessing step for image parsing algorithms. Superpixels are used as both regions of support for feature vectors and as a starting point for the final segmentation. Recent algorithms that construct superpixels that conform to a regular grid (or superpixel lattice) have used greedy solutions. In this paper we show that we can construct a globally optimal solution in either the horizontal or vertical direction using a single graph cut. The solution takes into account both edges in the image, and the coherence of the resulting superpixel regions. We show that our method outperforms existing algorithms for computing superpixel lattices. Additionally, we show that performance can be comparable or better than other contemporary segmentation algorithms which are not constrained to produce a lattice.
Proceedings of the 16th Ieee International Conference on Image Processing, 2009
We consider the problem of parsing facial features from an image labeling perspective. We learn a... more We consider the problem of parsing facial features from an image labeling perspective. We learn a per-pixel unary classifier, and a prior over expected label configurations, allowing us to estimate a dense labeling of facial images by part (e.g. hair, mouth, moustache, hat). This approach deals naturally with large variations in shape and appearance characteristic of unconstrained facial images, and also the problem of detecting classes that may be present or absent. We use an Adaboost-based unary classifier, and develop a group of priors based on 'epitomes' which are shown to be particularly effective in capturing the non-stationary aspects of face label distributions.
2010 20th International Conference on Pattern Recognition, 2010
In this study we propose a deformable pattern recognition method with CUDA implementation. In ord... more In this study we propose a deformable pattern recognition method with CUDA implementation. In order to achieve the proper correspondence between foreground pixels of input and prototype images, a pair of distance maps are generated from input and prototype images, whose pixel values are given based on the distance to the nearest foreground pixel. Then a regularization technique computes the horizontal and vertical displacements based on these distance maps. The dissimilarity is measured based on the eight-directional derivative of input and prototype images in order to leverage characteristic information on the curvature of line segments that might be lost after the deformation. The prototypeparallel displacement computation on CUDA and the gradual prototype elimination technique are employed for reducing the computational time without sacrificing the accuracy. A simulation shows that the proposed method with the k-nearest neighbor classifier gives the error rate of 0.57 % for the MNIST handwritten digit database.
In 2009 16th Ieee International Conference on Image Processing Vols 1 6 Ieee, 2009
Many previous studies have investigated gender classification in well-lit frontal images. In this... more Many previous studies have investigated gender classification in well-lit frontal images. In this paper we consider images where the pose, expression and lighting are relatively unconstrained. We localize faces using a standard sliding-window detector. We preprocess the facial region by convolving with Gabor filters at at four scales and four orientations. We sample these responses and concatenate them to form a feature vector. We develop a classifier based on an additive sum of non-linear functions of one-dimensional projections of the data. In particular we investigate arc tangent and weighted sums of Gaussians. We describe a training method based on increasing the binomial log likelihood. We demonstrate that our system on two databases and show that it performs well relative to the state of the art.
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
Subspace models have been very successful at modeling the appearance of structured image datasets... more Subspace models have been very successful at modeling the appearance of structured image datasets when the visual objects have been aligned in the images (e.g., faces). Even with extensions that allow for global transformations or dense warps of the image, the set of visual objects whose appearance may be modeled by such methods is limited. They are unable to account for visual objects where occlusion leads to changing visibility of different object parts (without a strict layered structure) and where a one-toone mapping between parts is not preserved. For example bunches of bananas contain different numbers of bananas but each individual banana shares an appearance subspace. In this work we remove the image space alignment limitations of existing subspace models by conditioning the models on a shape dependent context that allows for the complex, non-linear structure of the appearance of the visual object to be captured and shared. This allows us to exploit the advantages of subspace appearance models with non-rigid, deformable objects whilst also dealing with complex occlusions and varying numbers of parts. We demonstrate the effectiveness of our new model with examples of structured inpainting and appearance transfer.
Current face recognition algorithms require the tacit cooperation of users, who must position the... more Current face recognition algorithms require the tacit cooperation of users, who must position themselves in a small area of space and face the camera. Face recognition in uncontrolled conditions, such as in security camera footage presents two extra challenges. First, it is difficult to capture good quality images of faces in this setting. Second, the pose of the face is relatively uncontrolled which causes most face recognition algorithms to fail. In this paper, we present a series of solutions to address these problems. High quality face images are captured using a foveated wide field sensor, in which a narrow-field camera is directed towards faces using information from a static wide-field camera. Feature points corresponding to the eyes/nose etc. are accurately localized and face shape is normalized. A novel algorithm is introduced to identify these (typically non-frontal) faces from a test gallery of frontal faces. Results are demonstrated to be superior to contemporary approaches.
Horizontal disparity tuning for dynamic random-dot stereograms was investigated for a large popul... more Horizontal disparity tuning for dynamic random-dot stereograms was investigated for a large population of neurons (n = 787) in V1 of the awake macaque. Disparity sensitivity was quantified using a measure of the discriminability of the maximum and minimum points on the disparity tuning curve. This measure and others revealed a continuum of selectivity rather than separate populations of disparity- and nondisparity-sensitive neurons. Although disparity sensitivity was correlated with the degree of direction tuning, it was not correlated with other significant neuronal properties, including preferred orientation and ocular dominance. In accordance with the Gabor energy model, tuning curves for horizontal disparity were adequately described by Gabor functions when the neuron's orientation preference was near vertical. For neurons with orientation preferences near to horizontal, a Gaussian function was more frequently sufficient. The spatial frequency of the Gabor function that desc...
The responses of single cortical neurons were measured as a function of the binocular disparity o... more The responses of single cortical neurons were measured as a function of the binocular disparity of dynamic random dot stereograms for a large sample of neurons (n = 787) from V1 of the awake macaque. From this sample, we selected 180 neurons whose tuning curves were strongly tuned for disparity, well sampled and well described by one-dimensional Gabor functions. The fitted parameters of the Gabor functions were used to resolve three outstanding issues in binocular stereopsis. First, we considered whether tuning curves can be meaningfully divided into discrete tuning types. Careful examination of the distributions of the Gabor parameters that determine tuning shape revealed no evidence for clustering. We conclude that a continuum of tuning types is present. Second, we investigated the mechanism of disparity encoding for V1 neurons. The shape of the disparity tuning function can be used to distinguish between position-encoding (in which disparity is encoded by an interocular shift in ...
Proceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A major goal for face recognition is to identify faces where the pose of the probe is different f... more A major goal for face recognition is to identify faces where the pose of the probe is different from the stored face. Typical feature vectors vary more with pose than with identity, leading to very poor recognition performance. We propose a non-linear many-to-one mapping from a conventional feature space to a new space constructed so that each individual has a unique feature vector regardless of pose. Training data is used to implicitly parameterize the position of the multi-dimensional face manifold by pose. We introduce a co-ordinate transform, which depends on the position on the manifold. This transform is chosen so that different poses of the same face are mapped to the same feature vector. The same approach is applied to illumination changes. We investigate different methods for creating features, which are invariant to both pose and illumination. We provide a metric to assess the discriminability of the resulting features. Our technique increases the discriminability of faces...
Proceedings. International Symposium on Mixed and Augmented Reality, 2002
This paper presents an interactive theatre based on an embodied mixed reality space and wearable ... more This paper presents an interactive theatre based on an embodied mixed reality space and wearable computers. Embodied computing mixed reality spaces integrate ubiquitous computing, tangible interaction and social computing within a mixed reality space, which enables intuitive interaction with physical world and virtual world. We believe it has potential advantages to support novel interactive theatre experiences. Therefore, we explored the novel interactive theatre experience supported in the embodied mixed reality space, and implemented live 3D characters to interact with user in such a system.
IET 4th European Conference on Visual Media Production (CVMP 2007), 2007
Our goal is to generate novel photo-realistic images of a given object class (eg faces, trees) us... more Our goal is to generate novel photo-realistic images of a given object class (eg faces, trees) using a model trained from example images. To achieve this, we treat training images as samples from a texture with spatially varying statistics and synthesize using a modification of the patch-...
Proceedings of the 2002 ACM conference on Computer supported cooperative work - CSCW '02, 2002
We describe a real-time 3-D augmented reality video- conferencing system. With this technology, a... more We describe a real-time 3-D augmented reality video- conferencing system. With this technology, an observer sees the real world from his viewpoint, but modified so that the image of a remote collaborator is rendered into the scene. We register the image of the collaborator with the world by estimating the 3-D transformation between the camera and a fiducial marker. We
Many current face recognition algorithms perform badly when the lighting or pose of the probe and... more Many current face recognition algorithms perform badly when the lighting or pose of the probe and gallery images differ. In this paper we present a novel algorithm designed for these conditions. We describe face data as resulting from a generative model which incorporates both withinindividual and between-individual variation. In recognition we calculate the likelihood that the differences between face images are entirely due to within-individual variability. We extend this to the non-linear case where an arbitrary face manifold can be described and noise is position-dependent. We also develop a "tied" version of the algorithm that allows explicit comparison across quite different viewing conditions. We demonstrate that our model produces state of the art results for (i) frontal face recognition (ii) face recognition under varying pose.
Proceedings Cvpr Ieee Computer Society Conference on Computer Vision and Pattern Recognition Ieee Computer Society Conference on Computer Vision and Pattern Recognition, 2009
Contemporary face recognition algorithms rely on precise localization of keypoints (corner of eye... more Contemporary face recognition algorithms rely on precise localization of keypoints (corner of eye, nose etc.). Unfortunately, finding keypoints reliably and accurately remains a hard problem. In this paper we pose two questions. First, is it possible to exploit the gallery image in order to find keypoints in the probe image? For instance, consider finding the left eye in the probe image. Rather than using a generic eye model, we use a model that is informed by the appearance of the eye in the gallery image. To this end we develop a probabilistic model which combines recognition and keypoint localization. Second, is it necessary to localize keypoints? Alternatively we can consider keypoint position as a hidden variable which we marginalize over in a Bayesian manner. We demonstrate that both of these innovations improve performance relative to conventional methods in both frontal and cross-pose face recognition.
Proceedings Cvpr Ieee Computer Society Conference on Computer Vision and Pattern Recognition Ieee Computer Society Conference on Computer Vision and Pattern Recognition, 2010
Unsupervised over-segmentation of an image into superpixels is a common preprocessing step for im... more Unsupervised over-segmentation of an image into superpixels is a common preprocessing step for image parsing algorithms. Superpixels are used as both regions of support for feature vectors and as a starting point for the final segmentation. Recent algorithms that construct superpixels that conform to a regular grid (or superpixel lattice) have used greedy solutions. In this paper we show that we can construct a globally optimal solution in either the horizontal or vertical direction using a single graph cut. The solution takes into account both edges in the image, and the coherence of the resulting superpixel regions. We show that our method outperforms existing algorithms for computing superpixel lattices. Additionally, we show that performance can be comparable or better than other contemporary segmentation algorithms which are not constrained to produce a lattice.
Proceedings of the 16th Ieee International Conference on Image Processing, 2009
We consider the problem of parsing facial features from an image labeling perspective. We learn a... more We consider the problem of parsing facial features from an image labeling perspective. We learn a per-pixel unary classifier, and a prior over expected label configurations, allowing us to estimate a dense labeling of facial images by part (e.g. hair, mouth, moustache, hat). This approach deals naturally with large variations in shape and appearance characteristic of unconstrained facial images, and also the problem of detecting classes that may be present or absent. We use an Adaboost-based unary classifier, and develop a group of priors based on 'epitomes' which are shown to be particularly effective in capturing the non-stationary aspects of face label distributions.
2010 20th International Conference on Pattern Recognition, 2010
In this study we propose a deformable pattern recognition method with CUDA implementation. In ord... more In this study we propose a deformable pattern recognition method with CUDA implementation. In order to achieve the proper correspondence between foreground pixels of input and prototype images, a pair of distance maps are generated from input and prototype images, whose pixel values are given based on the distance to the nearest foreground pixel. Then a regularization technique computes the horizontal and vertical displacements based on these distance maps. The dissimilarity is measured based on the eight-directional derivative of input and prototype images in order to leverage characteristic information on the curvature of line segments that might be lost after the deformation. The prototypeparallel displacement computation on CUDA and the gradual prototype elimination technique are employed for reducing the computational time without sacrificing the accuracy. A simulation shows that the proposed method with the k-nearest neighbor classifier gives the error rate of 0.57 % for the MNIST handwritten digit database.
In 2009 16th Ieee International Conference on Image Processing Vols 1 6 Ieee, 2009
Many previous studies have investigated gender classification in well-lit frontal images. In this... more Many previous studies have investigated gender classification in well-lit frontal images. In this paper we consider images where the pose, expression and lighting are relatively unconstrained. We localize faces using a standard sliding-window detector. We preprocess the facial region by convolving with Gabor filters at at four scales and four orientations. We sample these responses and concatenate them to form a feature vector. We develop a classifier based on an additive sum of non-linear functions of one-dimensional projections of the data. In particular we investigate arc tangent and weighted sums of Gaussians. We describe a training method based on increasing the binomial log likelihood. We demonstrate that our system on two databases and show that it performs well relative to the state of the art.
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
Subspace models have been very successful at modeling the appearance of structured image datasets... more Subspace models have been very successful at modeling the appearance of structured image datasets when the visual objects have been aligned in the images (e.g., faces). Even with extensions that allow for global transformations or dense warps of the image, the set of visual objects whose appearance may be modeled by such methods is limited. They are unable to account for visual objects where occlusion leads to changing visibility of different object parts (without a strict layered structure) and where a one-toone mapping between parts is not preserved. For example bunches of bananas contain different numbers of bananas but each individual banana shares an appearance subspace. In this work we remove the image space alignment limitations of existing subspace models by conditioning the models on a shape dependent context that allows for the complex, non-linear structure of the appearance of the visual object to be captured and shared. This allows us to exploit the advantages of subspace appearance models with non-rigid, deformable objects whilst also dealing with complex occlusions and varying numbers of parts. We demonstrate the effectiveness of our new model with examples of structured inpainting and appearance transfer.
Current face recognition algorithms require the tacit cooperation of users, who must position the... more Current face recognition algorithms require the tacit cooperation of users, who must position themselves in a small area of space and face the camera. Face recognition in uncontrolled conditions, such as in security camera footage presents two extra challenges. First, it is difficult to capture good quality images of faces in this setting. Second, the pose of the face is relatively uncontrolled which causes most face recognition algorithms to fail. In this paper, we present a series of solutions to address these problems. High quality face images are captured using a foveated wide field sensor, in which a narrow-field camera is directed towards faces using information from a static wide-field camera. Feature points corresponding to the eyes/nose etc. are accurately localized and face shape is normalized. A novel algorithm is introduced to identify these (typically non-frontal) faces from a test gallery of frontal faces. Results are demonstrated to be superior to contemporary approaches.
Horizontal disparity tuning for dynamic random-dot stereograms was investigated for a large popul... more Horizontal disparity tuning for dynamic random-dot stereograms was investigated for a large population of neurons (n = 787) in V1 of the awake macaque. Disparity sensitivity was quantified using a measure of the discriminability of the maximum and minimum points on the disparity tuning curve. This measure and others revealed a continuum of selectivity rather than separate populations of disparity- and nondisparity-sensitive neurons. Although disparity sensitivity was correlated with the degree of direction tuning, it was not correlated with other significant neuronal properties, including preferred orientation and ocular dominance. In accordance with the Gabor energy model, tuning curves for horizontal disparity were adequately described by Gabor functions when the neuron's orientation preference was near vertical. For neurons with orientation preferences near to horizontal, a Gaussian function was more frequently sufficient. The spatial frequency of the Gabor function that desc...
The responses of single cortical neurons were measured as a function of the binocular disparity o... more The responses of single cortical neurons were measured as a function of the binocular disparity of dynamic random dot stereograms for a large sample of neurons (n = 787) from V1 of the awake macaque. From this sample, we selected 180 neurons whose tuning curves were strongly tuned for disparity, well sampled and well described by one-dimensional Gabor functions. The fitted parameters of the Gabor functions were used to resolve three outstanding issues in binocular stereopsis. First, we considered whether tuning curves can be meaningfully divided into discrete tuning types. Careful examination of the distributions of the Gabor parameters that determine tuning shape revealed no evidence for clustering. We conclude that a continuum of tuning types is present. Second, we investigated the mechanism of disparity encoding for V1 neurons. The shape of the disparity tuning function can be used to distinguish between position-encoding (in which disparity is encoded by an interocular shift in ...
Proceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A major goal for face recognition is to identify faces where the pose of the probe is different f... more A major goal for face recognition is to identify faces where the pose of the probe is different from the stored face. Typical feature vectors vary more with pose than with identity, leading to very poor recognition performance. We propose a non-linear many-to-one mapping from a conventional feature space to a new space constructed so that each individual has a unique feature vector regardless of pose. Training data is used to implicitly parameterize the position of the multi-dimensional face manifold by pose. We introduce a co-ordinate transform, which depends on the position on the manifold. This transform is chosen so that different poses of the same face are mapped to the same feature vector. The same approach is applied to illumination changes. We investigate different methods for creating features, which are invariant to both pose and illumination. We provide a metric to assess the discriminability of the resulting features. Our technique increases the discriminability of faces...
Proceedings. International Symposium on Mixed and Augmented Reality, 2002
This paper presents an interactive theatre based on an embodied mixed reality space and wearable ... more This paper presents an interactive theatre based on an embodied mixed reality space and wearable computers. Embodied computing mixed reality spaces integrate ubiquitous computing, tangible interaction and social computing within a mixed reality space, which enables intuitive interaction with physical world and virtual world. We believe it has potential advantages to support novel interactive theatre experiences. Therefore, we explored the novel interactive theatre experience supported in the embodied mixed reality space, and implemented live 3D characters to interact with user in such a system.
IET 4th European Conference on Visual Media Production (CVMP 2007), 2007
Our goal is to generate novel photo-realistic images of a given object class (eg faces, trees) us... more Our goal is to generate novel photo-realistic images of a given object class (eg faces, trees) using a model trained from example images. To achieve this, we treat training images as samples from a texture with spatially varying statistics and synthesize using a modification of the patch-...
Proceedings of the 2002 ACM conference on Computer supported cooperative work - CSCW '02, 2002
We describe a real-time 3-D augmented reality video- conferencing system. With this technology, a... more We describe a real-time 3-D augmented reality video- conferencing system. With this technology, an observer sees the real world from his viewpoint, but modified so that the image of a remote collaborator is rendered into the scene. We register the image of the collaborator with the world by estimating the 3-D transformation between the camera and a fiducial marker. We
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