Yui Man Lui
University of Central Missouri, Computer Science, Faculty Member
Research Interests:
Research Interests:
Research Interests:
Research Interests:
... are with the De-partment of Computer Science, Colorado State University, Fort Collins, CO 80523, USA {lui}{ross}{whitley}@cs.colostate. ... Isard and Blake [4] propose a condensationalgorithm for visual tracking using active contours... more
... are with the De-partment of Computer Science, Colorado State University, Fort Collins, CO 80523, USA {lui}{ross}{whitley}@cs.colostate. ... Isard and Blake [4] propose a condensationalgorithm for visual tracking using active contours parameterized by low dimensional vectors. ...
Research Interests:
ABSTRACT Face recognition involves at least three major concepts from statistics: dimension reduction, feature extraction, and prediction. A selective review of algorithms, from seminal to state-of-the-art, explores how these concepts... more
ABSTRACT Face recognition involves at least three major concepts from statistics: dimension reduction, feature extraction, and prediction. A selective review of algorithms, from seminal to state-of-the-art, explores how these concepts persist as organizing principles in the field. Algorithms based directly upon classical statistical techniques include linear methods like principal component analysis and linear discriminant analysis. Nonlinear manifold methods, such as Laplacianfaces and Stiefel quotients, offer considerable performance improvements. Other noteworthy ideas include three-dimensional morphable models, methods using local regions and/or alternative feature spaces (e.g., elastic bunch graph matching and local binary patterns) and sparse representation approaches. Opportunities for innovative statistical and collaborative research in face recognition are expanding in tandem with the growing complexity and diversity of applications. WIREs Comput Stat 2013, 5:288–308. doi: 10.1002/wics.1262 Conflict of interest: The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website
Research Interests:
Research Interests:
Research Interests:
Face registration is a challenging problem due in part to the non-rigid nature of human faces. Active Appearance Models (AAMs) have been proposed as a useful technique for face registration in part because they can account for changes in... more
Face registration is a challenging problem due in part to the non-rigid nature of human faces. Active Appearance Models (AAMs) have been proposed as a useful technique for face registration in part because they can account for changes in shape. Fitting of AAMs to imagery is typically done using the Gauss-Newton method; however this approach is known to fail when
Research Interests:
Research Interests:
We present an adaptive framework for condensation algorithms in the context of human face tracking. We attack the face tracking problem by making factored sampling more efficient and the appearance update more effective. An adaptive... more
We present an adaptive framework for condensation algorithms in the context of human face tracking. We attack the face tracking problem by making factored sampling more efficient and the appearance update more effective. An adaptive affine cascade factored sampling strategy is introduced to sample the parameter space such that coarse face locations are located first followed by a fine factored sampling with a small number of particles. In addition, local linearity of an appearance manifold is used in conjunction with a new criterion to select a tangent plane for updating an appearance in face tracking. Our proposed method seeks the best linear variety from the selected tangent plane to form a reference image. Finally, we demonstrate the effectiveness and efficiency of the proposed method on four challenging videos. These test video sequences show that our method is robust to illumination, appearance, and pose changes, as well as temporary occlusions.
Research Interests:
An image-set based face recognition algorithm is proposed that exploits the full geometrical interpretation of Canonical Correlation Analysis (CCA). CCA maximizes the correlation between two linear subspaces associated with image-sets,... more
An image-set based face recognition algorithm is proposed that exploits the full geometrical interpretation of Canonical Correlation Analysis (CCA). CCA maximizes the correlation between two linear subspaces associated with image-sets, where an image-set is assumed to contain multiple images of a person's face. When these linear subspaces are viewed as points on a Grassmann manifold, then geodesic distance on the manifold becomes the natural way to compare image-sets. The proposed method is tested on the ORL data set where it achieves a rank one identification rate of 98.75%. The proposed method is also tested on a subset of the Face Recognition Grand Challenge Experiment 4 data. Specifically, 82 probe and 230 gallery subjects with 32 images per probe and gallery image-set. Our algorithm achieves a rank one identification rate of 87% and a verification rate of 81% at a false accept rate of 1/1;000. These results on FRGC are significantly better than the well-known image-set matc...
This paper presents a meta-analysis for covariates that affect performance of face recognition algorithms. Our review of the literature found six covariates for which multiple studies reported effects on face recognition performance.... more
This paper presents a meta-analysis for covariates that affect performance of face recognition algorithms. Our review of the literature found six covariates for which multiple studies reported effects on face recognition performance. These are: age of the person, elapsed time between images, gender of the person, the person's expression, the resolution of the face images, and the race of the person. The results presented are drawn from 25 studies conducted over the past 12 years. There is near complete agreement between all of the studies that older people are easier to recognize than younger people, and recognition performance begins to degrade when images are taken more than a year apart. While individual studies find men or women easier to recognize, there is no consistent gender effect. There is universal agreement that changing expression hurts recognition performance. If forced to compare different expressions, there is still insufficient evidence to conclude that any part...
Research Interests:
Computer-aided mammography is an important and challenging task in automated diagnosis. It has great potential over the traditional x-ray mammography in terms of efficiency and accuracy. Microcalcifications are the earliest sign of breast... more
Computer-aided mammography is an important and challenging task in automated diagnosis. It has great potential over the traditional x-ray mammography in terms of efficiency and accuracy. Microcalcifications are the earliest sign of breast carcinomas and their accurate detection is one of the key issues for breast cancer control. In this study, a novel approach to detecting microcalcifications in digital mammograms
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
ABSTRACT
Research Interests:
Research Interests:
Videos can be naturally represented as multidimensional arrays known as tensors. However, the geometry of the tensor space is often ignored. In this paper, we argue that the underlying geometry of the tensor space is an important property... more
Videos can be naturally represented as multidimensional arrays known as tensors. However, the geometry of the tensor space is often ignored. In this paper, we argue that the underlying geometry of the tensor space is an important property for action classification. We characterize a tensor as a point on a product manifold and perform classification on this space. First, we
Research Interests:
Research Interests:
Research Interests:
ABSTRACT This paper presents an extremely simple human detection algorithm based on correlating edge magnitude images with a filter. The key is the technology used to train the filter: average of synthetic exact filters (ASEF). The ASEF... more
ABSTRACT This paper presents an extremely simple human detection algorithm based on correlating edge magnitude images with a filter. The key is the technology used to train the filter: average of synthetic exact filters (ASEF). The ASEF based detector can process images at over 25 frames per second and achieves a 94.5% detection rate with less than one false detection per frame for sparse crowds. Filter training is also fast, taking only 12 seconds to train the detector on 32 manually annotated images. Evaluation is performed on the PETS 2009 dataset and results are compared to the OpenCV cascade classifier and a state-of-the-art deformable parts based person detector.
Research Interests:
Research Interests:
The Good, the Bad, & the Ugly Face Challenge Problem was created to encourage the development of algo- rithms that are robust to recognition across changes that occur in still... more
The Good, the Bad, & the Ugly Face Challenge Problem was created to encourage the development of algo- rithms that are robust to recognition across changes that occur in still frontal faces. The Good, the Bad, & the Ugly consists of three partitions. The Good partition contains pairs of images that are considered easy to recognize. On the Good partition,
Research Interests:
... Chan-Su Lee Yeungnam University South Korea chansu@ynu.ac.kr Yui Man Lui Colorado State University Fort Collins, CO USA lui@cs.colostate.edu ... More information about ma-trix manifold and its application in computer vision can be... more
... Chan-Su Lee Yeungnam University South Korea chansu@ynu.ac.kr Yui Man Lui Colorado State University Fort Collins, CO USA lui@cs.colostate.edu ... More information about ma-trix manifold and its application in computer vision can be found from the review paper by Lui [11]. ...
Research Interests:
... The descriptors were modeled hierarchically and multiple kernel learning was exploited to characterize each action. ... tangent bundle methods perform well on the Cambridge gesture and the KTH human action datasets. ... Action... more
... The descriptors were modeled hierarchically and multiple kernel learning was exploited to characterize each action. ... tangent bundle methods perform well on the Cambridge gesture and the KTH human action datasets. ... Action localization is the prior step for action classifica-tion ...
Research Interests:
Image thresholds can be chosen by minimizing the measures of fuzziness. However, there are no existing algorithms selecting the bandwidth of fuzzy membership functions automatically. In this paper, a new method for automatic bandwidth... more
Image thresholds can be chosen by minimizing the measures of fuzziness. However, there are no existing algorithms selecting the bandwidth of fuzzy membership functions automatically. In this paper, a new method for automatic bandwidth selection of fuzzy membership functions is presented. First, the peak locations are chosen from the histogram using the peak selection criterion. The suitable bandwidth is found
Research Interests:
ABSTRACT
Research Interests:
Research Interests:
Research Interests:
Research Interests:
We present an adaptive framework for condensation algorithms in the context of human-face tracking. We attack the face tracking problem by making factored sampling more efficient and appearance update more effective. An adaptive affine... more
We present an adaptive framework for condensation algorithms in the context of human-face tracking. We attack the face tracking problem by making factored sampling more efficient and appearance update more effective. An adaptive affine cascade factored sampling strategy is introduced to sample the parameter space such that coarse face locations are located first, followed by a fine factored sampling with
Research Interests:
Research Interests:
Many electronic watermarks for still images and video content are sensitive to geometric distortions. For example, simple rotation, scaling, and/or translation (RST) of an image can prevent blind detection of a public watermark. In this... more
Many electronic watermarks for still images and video content are sensitive to geometric distortions. For example, simple rotation, scaling, and/or translation (RST) of an image can prevent blind detection of a public watermark. In this paper, we propose a watermarking algorithm that is robust to RST distortions. The watermark is embedded into a one-dimensional (1-D) signal obtained by taking the Fourier transform of the image, resampling the Fourier magnitudes into log-polar coordinates, and then summing a function of those magnitudes along the log-radius axis. Rotation of the image results in a cyclical shift of the extracted signal. Scaling of the image results in amplification of the extracted signal, and translation of the image has no effect on the extracted signal. We can therefore compensate for rotation with a simple search, and compensate for scaling by using the correlation coefficient as the detection measure. False positive results on a database of 10,000 images are reported. Robustness results on a database of 2000 images are described. It is shown that the watermark is robust to rotation, scale, and translation. In addition, we describe tests examining the watermarks resistance to cropping and JPEG compression.
Research Interests: Cognitive Science, Image Processing, Signal Processing, Video, Watermarking, and 14 moreImages, Scale, Detectors, Robustness, IEEE Transactions, Fourier transform, Filtering, Rotation, Fourier Transforms, Electrical And Electronic Engineering, Fourier Transformation, Resampling, Cropping, and Information Protection
Increasingly, machines are interacting with people through human action recognition from video streams. Video data can naturally be represented as a third-order data tensor. Although many tensor-based approaches have been proposed for... more
Increasingly, machines are interacting with people through human action recognition from video streams. Video data can naturally be represented as a third-order data tensor. Although many tensor-based approaches have been proposed for action recognition, the geometry of the tensor space is seldom regarded as an important aspect. In this paper, we stress that a data tensor is related to a
Research Interests:
ABSTRACT The field of biometric face recognition blends methods from computer science, engineering and statistics, however statistical reasoning has been applied predominantly in the design of recognition algorithms. A new opportunity for... more
ABSTRACT The field of biometric face recognition blends methods from computer science, engineering and statistics, however statistical reasoning has been applied predominantly in the design of recognition algorithms. A new opportunity for the application of statistical methods is driven by growing interest in biometric performance evaluation. Methods for performance evaluation seek to identify, compare and interpret how characteristics of subjects, the environment and images are associated with the performance of recognition algorithms. Some central topics in face recognition are reviewed for background and several examples of recognition algorithms are given. One approach to the evaluation problem is then illustrated with a generalized linear mixed model analysis of the Good, Bad, and Ugly Face Challenge, a pre-eminent face recognition dataset used to test state-of-the-art still-image face recognition algorithms. Findings include that (i) between-subject variation is the dominant source of verification heterogeneity when algorithm performance is good, and (ii) many covariate effects on verification performance are ‘universal’ across easy, medium and hard verification tasks. Although the design and evaluation of face recognition algorithms draw upon some familiar statistical ideas in multivariate statistics, dimension reduction, classification, clustering, binary response data, generalized linear models and random effects, the field also presents some unique features and challenges. Opportunities abound for innovative statistical work in this new field.
Research Interests:
Videos can be naturally represented as multidimensional arrays known as tensors. However, the geometry of the tensor space is often ignored. In this paper, we argue that the underlying geometry of the tensor space is an important property... more
Videos can be naturally represented as multidimensional arrays known as tensors. However, the geometry of the tensor space is often ignored. In this paper, we argue that the underlying geometry of the tensor space is an important property for action classification. We characterize a tensor as a point on a product manifold and perform classification on this space. First, we
Research Interests:
Many electronic watermarks for still images and video content are sensitive to geometric distortions. For example,simple rotation, scaling, and/or translation (RST) of an image can prevent detection of a public watermark. In thispaper, we... more
Many electronic watermarks for still images and video content are sensitive to geometric distortions. For example,simple rotation, scaling, and/or translation (RST) of an image can prevent detection of a public watermark. In thispaper, we propose a watermarking algorithm that is robust to RST distortions. The watermark is embedded into a1-dimensional signal obtained by first taking the Fourier transform of the
Research Interests:
The attention paid to matrix manifolds has grown considerably in the computer vision community in recent years. There are a wide range of important applications including face recognition, action recognition, clustering, visual tracking,... more
The attention paid to matrix manifolds has grown considerably in the computer vision community in recent years. There are a wide range of important applications including face recognition, action recognition, clustering, visual tracking, and motion grouping and segmentation. The increased popularity of matrix manifolds is due partly to the need to characterize image features in non-Euclidean spaces. Matrix manifolds provide rigorous formulations allowing patterns to be naturally expressed and classified in a particular parameter space. This paper gives an overview of common matrix manifolds employed in computer vision and presents a summary of related applications. Researchers in computer vision should find this survey beneficial due to the overview of matrix manifolds, the discussion as well as the collective references.