Results 11 - 20
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136
Shape-based hand recognition
- IEEE TRANSACTIONS ON IMAGE PROCESSING
, 2006
"... The problem of person recognition and verification based on their hand images has been addressed. The system is based on the images of the right hands of the subjects, captured by a flatbed scanner in an unconstrained pose at 45 dpi. In a preprocessing stage of the algorithm, the silhouettes of han ..."
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Cited by 46 (4 self)
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The problem of person recognition and verification based on their hand images has been addressed. The system is based on the images of the right hands of the subjects, captured by a flatbed scanner in an unconstrained pose at 45 dpi. In a preprocessing stage of the algorithm, the silhouettes of hand images are registered to a fixed pose, which involves both rotation and translation of the hand and, separately, of the individual fingers. Two feature sets have been comparatively assessed, Hausdorff distance of the hand contours and independent component features of the hand silhouette images. Both the classification and the verification performances are found to be very satisfactory as it was shown that, at least for groups of about five hundred subjects, hand-based recognition is a viable secure access control scheme.
Face Recognition in Subspaces
- IN: S.Z. LI, A.K. JAIN (EDS.), HANDBOOK OF FACE RECOGNITION
, 2004
"... Images of faces, represented as high-dimensional pixel arrays, often belong to a manifold of intrinsically low dimension. Face recognition, and computer vision research in general, has witnessed a growing interest in techniques that capitalize on this observation, and apply algebraic and statisti ..."
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Cited by 44 (0 self)
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Images of faces, represented as high-dimensional pixel arrays, often belong to a manifold of intrinsically low dimension. Face recognition, and computer vision research in general, has witnessed a growing interest in techniques that capitalize on this observation, and apply algebraic and statistical tools for extraction and analysis of the underlying manifold. In this chapter we describe in roughly chronological order techniques that identify, parameterize and analyze linear and nonlinear subspaces, from the original Eigenfaces technique to the recently introduced Bayesian method for probabilistic similarity analysis, and discuss comparative experimental evaluation of some of these techniques. We also discuss practical issues related to the application of subspace methods for varying pose, illumination and expression.
Combining Classifiers For Face Recognition
, 2003
"... Current two-dimensional face recognition approaches can obtain a good performance only under constrained environments. However, in the real applications, face appearance changes significantly due to different illumination, pose, and expression. Face recognizers based on different representations of ..."
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Cited by 42 (2 self)
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Current two-dimensional face recognition approaches can obtain a good performance only under constrained environments. However, in the real applications, face appearance changes significantly due to different illumination, pose, and expression. Face recognizers based on different representations of the input face images have different sensitivity to these variations. Therefore, a combination of different face classifiers which can integrate the complementary information should lead to improved classification accuracy. We use the sum rule and RBF-based integration strategies to combine three commonly used face classifiers based on PCA, ICA and LDA representations. Experiments conducted on a face database containing 206 subjects (2,060 face images) show that the proposed classifier combination approaches outperform individual classifiers.
PCA vs. ICA: a comparison on the FERET data set
- in Proc. of the 4th International Conference on Computer Vision, ICCV’02
, 2002
"... Over the last ten years, face recognition has become a specialized applications area within the field of computer vision. Sophisticated commercial systems have been developed that achieve high recognition rates. Although elaborate, many of these systems include a subspace projection step and a neare ..."
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Cited by 34 (5 self)
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Over the last ten years, face recognition has become a specialized applications area within the field of computer vision. Sophisticated commercial systems have been developed that achieve high recognition rates. Although elaborate, many of these systems include a subspace projection step and a nearest neighbor classifier. The goal of this paper is to rigorously compare two subspace projection techniques within the context of a baseline system on the face recognition task. The first technique is principal component analysis (PCA), a well-known “baseline ” for projection techniques. The second technique is independent component analysis (ICA), a newer method that produces spatially localized and statistically independent basis vectors. Testing on the FERET data set (and using standard partitions), we find that, when a proper distance metric is used, PCA significantly outperforms ICA on a human face recognition task. This is contrary to previously published results. 1.
A new sparse image representation algorithm applied to facial expression recognition
- Proc. IEEE Workshop on Machine Learning for Signal Processing
, 2004
"... Abstract. In this paper we present a novel algorithm for learning facial expressions in a supervised manner. This algorithm is derived from the local non-negative matrix factorization (LNMF) algorithm, which is an extension of non-negative matrix factorization (NMF) method. We call this newly propos ..."
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Cited by 30 (11 self)
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Abstract. In this paper we present a novel algorithm for learning facial expressions in a supervised manner. This algorithm is derived from the local non-negative matrix factorization (LNMF) algorithm, which is an extension of non-negative matrix factorization (NMF) method. We call this newly proposed algorithm Discriminant Non-negative Matrix Factorization (DNMF). Given an image database, all these three algorithms decompose the database into basis images and their corresponding coefficients. This decomposition is computed differently for each method. The decomposition results are applied on facial images for the recognition of the six basic facial expressions. We found that our algorithm shows superior performance by achieving a higher recognition rate, when compared to NMF and LNMF.
Face Recognition Using Kernel Methods
, 2001
"... Principal Component Analysis and Fisher Linear Discriminant methods have demonstrated their success in face detection, recognition, and tracking. The representation in these subspace methods is based on second order statistics of the image set, and does not address higher order statistical dependenc ..."
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Cited by 28 (0 self)
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Principal Component Analysis and Fisher Linear Discriminant methods have demonstrated their success in face detection, recognition, and tracking. The representation in these subspace methods is based on second order statistics of the image set, and does not address higher order statistical dependencies such as the relationships among three or more pixels. Recently Higher Order Statistics and Independent Component Analysis (ICA) have been used as informative low dimensional representations for visual recognition.
Fusion of global and local information for object detection
- In ICPR’02
, 2002
"... This paper presents a framework for fusing together global and local information in images to form a powerful object detection system. We begin by describing two detection algorithms. The first algorithm uses independent component analysis (ICA) to derive an image representation that captures global ..."
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Cited by 24 (0 self)
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This paper presents a framework for fusing together global and local information in images to form a powerful object detection system. We begin by describing two detection algorithms. The first algorithm uses independent component analysis (ICA) to derive an image representation that captures global information in the input data. The second algorithm uses a part-based representation that relies on local properties of the data. The strengths of the two detection algorithms are then combined to form a more powerful detector. The approach is evaluated on a database of real-world images containing side views of cars. The combined detector gives distinctly superior performance than each of the individual detectors, achieving a high detection accuracy of 94 % on this difficult test set. 1
Automatic 3D face detection, normalization and recognition
- In 3DPVT
, 2006
"... A fully automatic 3D face recognition algorithm is presented. Several novelties are introduced to make the recognition robust to facial expressions and efficient. These novelties include: (1) Automatic 3D face detection by detecting the nose; (2) Automatic pose correction and normalization of the 3D ..."
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Cited by 23 (6 self)
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A fully automatic 3D face recognition algorithm is presented. Several novelties are introduced to make the recognition robust to facial expressions and efficient. These novelties include: (1) Automatic 3D face detection by detecting the nose; (2) Automatic pose correction and normalization of the 3D face as well as its corresponding 2D face using the Hotelling Transform; (3) A Spherical Face Representation and its use as a rejection classifier to quickly reject a large number of candidate faces for efficient recognition; and (4) Robustness to facial expressions by automatically segmenting the face into expression sensitive and insensitive regions. Experiments performed on the FRGC Ver 2.0 dataset (9,500 2D/3D faces) show that our algorithm outperforms existing 3D recognition algorithms. We achieved verification rates of 99.47 % and 94.09 % at 0.001 FAR and identification rates of 98.03 % and 89.25 % for probes with neutral and non-neutral expression respectively. 1.
Learning Multiview Face Subspaces and Facial Pose Estimation using Independent Component Analysis
- IEEE Trans. Image Processing
, 2005
"... Abstract—An independent component analysis (ICA) based ap-proach is presented for learning view-specific subspace representa-tions of the face object from multiview face examples. ICA, its vari-ants, namely independent subspace analysis (ISA) and topographic independent component analysis (TICA), ta ..."
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Cited by 17 (0 self)
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Abstract—An independent component analysis (ICA) based ap-proach is presented for learning view-specific subspace representa-tions of the face object from multiview face examples. ICA, its vari-ants, namely independent subspace analysis (ISA) and topographic independent component analysis (TICA), take into account higher order statistics needed for object view characterization. In con-trast, principal component analysis (PCA), which de-correlates the second order moments, can hardly reveal good features for charac-terizing different views, when the training data comprises a mix-ture of multiview examples and the learning is done in an unsu-pervised way with view-unlabeled data. We demonstrate that ICA, TICA, and ISA are able to learn view-specific basis components un-supervisedly from the mixture data. We investigate results learned by ISA in an unsupervised way closely and reveal some surprising findings and thereby explain underlying reasons for the emergent formation of view subspaces. Extensive experimental results are presented. Index Terms—Appearance-based approach, face analysis, inde-pendent component analysis (ICA), independent subspace analysis (ISA), learning by examples, topographic independent component analysis (TICA), view subspaces. I.
