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141
Human Computing and Machine Understanding of Human Behavior: A Survey
- SURVEY, PROC. ACM INT’L CONF. MULTIMODAL INTERFACES
, 2006
"... A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should b ..."
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Cited by 132 (33 self)
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A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, human-like interactive functions including understanding and emulating certain human behaviors such as affective and social signaling. This article discusses a number of components of human behavior, how they might be integrated into computers, and how far we are from realizing the front end of human computing, that is, how far are we from enabling computers to understand human behavior.
Multilinear principal component analysis of tensor objects for recognition
- in Proc. Int. Conf. Pattern Recognit
, 2006
"... Abstract—This paper introduces a multilinear principal component analysis (MPCA) framework for tensor object feature extraction. Objects of interest in many computer vision and pattern recognition applications, such as 2-D/3-D images and video sequences are naturally described as tensors or multilin ..."
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Cited by 88 (15 self)
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Abstract—This paper introduces a multilinear principal component analysis (MPCA) framework for tensor object feature extraction. Objects of interest in many computer vision and pattern recognition applications, such as 2-D/3-D images and video sequences are naturally described as tensors or multilinear arrays. The proposed framework performs feature extraction by determining a multilinear projection that captures most of the original tensorial input variation. The solution is iterative in nature and it proceeds by decomposing the original problem to a series of multiple projection subproblems. As part of this work, methods for subspace dimensionality determination are proposed and analyzed. It is shown that the MPCA framework discussed in this work supplants existing heterogeneous solutions such as the classical principal component analysis (PCA) and its 2-D variant (2-D PCA). Finally, a tensor object recognition system is proposed with the introduction of a discriminative tensor feature selection mechanism and a novel classification strategy, and applied to the problem of gait recognition. Results presented here indicate MPCA’s utility as a feature extraction tool. It is shown that even without a fully optimized design, an MPCA-based gait recognition module achieves highly competitive performance and compares favorably to the state-of-the-art gait recognizers. Index Terms—Dimensionality reduction, feature extraction, gait recognition, multilinear principal component analysis (MPCA), tensor objects. I.
A Region Ensemble for 3-D Face Recognition
, 2008
"... In this paper, we introduce a new system for 3-D face recognition based on the fusion of results from a committee of regions that have been independently matched. Experimental results demonstrate that using 28 small regions on the face allow for the highest level of 3-D face recognition. Score-base ..."
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Cited by 48 (2 self)
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In this paper, we introduce a new system for 3-D face recognition based on the fusion of results from a committee of regions that have been independently matched. Experimental results demonstrate that using 28 small regions on the face allow for the highest level of 3-D face recognition. Score-based fusion is performed on the individual region match scores and experimental results show that the Borda count and consensus voting methods yield higher performance than the standard sum, product, and min fusion rules. In addition, results are reported that demonstrate the robustness of our algorithm by simulating large holes and artifacts in images. To our knowledge, no other work has been published that uses a large number of 3-D face regions for high-performance face matching. Rank one recognition rates of 97.2 % and verification rates of 93.2 % at a 0.1 % false accept rate are reported and compared to other methods published on the face recognition grand challenge v2 data set.
Expression-Invariant Representations of Faces
- IEEE TRANS. PAMI
, 2007
"... Addressed here is the problem of constructing and analyzing expression-invariant representations of human faces. We demonstrate and justify experimentally a simple geometric model that allows to describe facial expressions as isometric deformations of the facial surface. The main step in the constru ..."
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Cited by 44 (6 self)
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Addressed here is the problem of constructing and analyzing expression-invariant representations of human faces. We demonstrate and justify experimentally a simple geometric model that allows to describe facial expressions as isometric deformations of the facial surface. The main step in the construction of expression-invariant representation of a face involves embedding of the facial intrinsic geometric structure into some convenient low-dimensional space. We study the influence of the embedding space geometry and dimensionality choice on the representation accuracy and argue that compared to its Euclidean counterpart, spherical embedding leads to notably smaller metric distortions. We experimentally support our claim showing that a smaller embedding error leads to better recognition.
3D Deformable Face Tracking with a Commodity Depth Camera
"... Abstract. Recently, there has been an increasing number of depth cameras available at commodity prices. These cameras can usually capture both color and depth images in real-time, with limited resolution and accuracy. In this paper, we study the problem of 3D deformable face tracking with such commo ..."
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Cited by 37 (5 self)
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Abstract. Recently, there has been an increasing number of depth cameras available at commodity prices. These cameras can usually capture both color and depth images in real-time, with limited resolution and accuracy. In this paper, we study the problem of 3D deformable face tracking with such commodity depth cameras. A regularized maximum likelihood deformable model fitting (DMF) algorithm is developed, with special emphasis on handling the noisy input depth data. In particular, we present a maximum likelihood solution that can accommodate sensor noise represented by an arbitrary covariance matrix, which allows more elaborate modeling of the sensor’s accuracy. Furthermore, an ℓ1 regularization scheme is proposed based on the semantics of the deformable face model, which is shown to be very effective in improving the tracking results. To track facial movement in subsequent frames, feature points in the texture images are matched across frames and integrated into the DMF framework seamlessly. The effectiveness of the proposed method is demonstrated with multiple sequences with ground truth information. 1
Real-time face pose estimation from single range images
- In CVPR
, 2008
"... We present a real-time algorithm to estimate the 3D pose of a previously unseen face from a single range im-age. Based on a novel shape signature to identify noses in range images, we generate candidates for their positions, and then generate and evaluate many pose hypotheses in parallel using moder ..."
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Cited by 31 (1 self)
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We present a real-time algorithm to estimate the 3D pose of a previously unseen face from a single range im-age. Based on a novel shape signature to identify noses in range images, we generate candidates for their positions, and then generate and evaluate many pose hypotheses in parallel using modern graphics processing units (GPUs). We developed a novel error function that compares the in-put range image to precomputed pose images of an average face model. The algorithm is robust to large pose variations of±90 ◦ yaw,±45 ◦ pitch and±30 ◦ roll rotation, facial ex-pression, partial occlusion, and works for multiple faces in the field of view. It correctly estimates 97.8 % of the poses within yaw and pitch error of 15 ◦ at 55.8 fps. To evalu-ate the algorithm, we built a database of range images with large pose variations and developed a method for automatic ground truth annotation. 1.
3-d face recognition with the geodesic polar representation
- IEEE Transactions on Information Forensics and Security
"... Abstract—The performance of automatic 3-D face recognition can be significantly improved by coping with the nonrigidity of the facial surface. In this paper, we propose a geodesic polar parameterization of the face surface. With this parameterization, the intrinsic surface attributes do not change u ..."
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Cited by 25 (5 self)
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Abstract—The performance of automatic 3-D face recognition can be significantly improved by coping with the nonrigidity of the facial surface. In this paper, we propose a geodesic polar parameterization of the face surface. With this parameterization, the intrinsic surface attributes do not change under isometric deformations and, therefore, the proposed representation is appropriate for expression-invariant 3-D face recognition. We also consider the special case of an open mouth that violates the isometry assumption and propose a modified geodesic polar parameterization that also leads to invariant representation. Based on this parameterization, 3-D face recognition is reduced to the classification of expression-compensated 2-D images that can be classified with state-of-the-art algorithms. Experimental results verify theoretical assumptions and demonstrate the benefits of the geodesic polar parameterization on 3-D face recognition. Index Terms—Face recognition, geodesic polar coordinates, isometric mapping, range images. I.
3D Face Recognition using iso-Geodesic Stripes
, 2010
"... In this paper, we present a novel approach to 3D face matching that shows high effectiveness in distinguishing facial differences between distinct individuals from differences induced by non-neutral expressions within the same individual. The approach takes into account geometrical information of t ..."
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Cited by 25 (3 self)
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In this paper, we present a novel approach to 3D face matching that shows high effectiveness in distinguishing facial differences between distinct individuals from differences induced by non-neutral expressions within the same individual. The approach takes into account geometrical information of the 3D face and encodes the relevant information into a compact representation in the form of a graph. Nodes of the graph represent equal width iso-geodesic facial stripes. Arcs between pairs of nodes are labeled with descriptors, referred to as 3D Weighted Walkthroughs (3DWWs), that capture the mutual relative spatial displacement between all the pairs of points of the corresponding stripes. Face partitioning into iso-geodesic stripes and 3DWWs together provide an approximate representation of local morphology of faces that exhibits smooth variations for changes induced by facial expressions. The graph-based representation permits very efficient matching for face recognition and is also suited to be employed for face identification in very large datasets with the support of appropriate index structures. The method obtained the best ranking at the SHREC 2008 contest for 3D face recognition. We present an extensive comparative evaluation of performance with the FRGC v2.0 dataset and the SHREC08 dataset.
Representation Plurality and Fusion for 3D Face Recognition
, 2007
"... In this paper, we present an extensive study of 3D face recognition algorithms and examine the benefits of various score, rank and decision-level fusion rules. We investigate face recognizers from two perspectives: the data representation techniques used, and the feature extraction algorithms that m ..."
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Cited by 24 (4 self)
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In this paper, we present an extensive study of 3D face recognition algorithms and examine the benefits of various score, rank and decision-level fusion rules. We investigate face recognizers from two perspectives: the data representation techniques used, and the feature extraction algorithms that matches best each representation type. We also consider novel applications of various feature extraction techniques such as Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT), Non-negative matrix factorization (NMF), and principal curvature directions to the shape modality. We discuss and compare various classifier combination methods such as fixed rules, voting-based and rank-based fusion schemes. We also present a dynamic confidence estimation algorithm to boost the fusion performance. In identification experiments performed on FRGC v1.0 and FRGC v2.0 face databases, we have tried to find the answers to the following questions: 1) The relative importance of the face representation techniques vis-à-vis the types of features extracted, 2) The impact of the gallery size, 3) The conditions under which subspace methods are preferable and the compression factor; 4) The most advantageous fusion level and fusion methods; 5) The role of confidence votes in improving fusion and the style of selecting experts in the fusion; 6) The consistency of the conclusions across different databases.
Bilinear models for 3d face and facial expression recognition
- IEEE Transactions on Information Forensics and Security
, 2008
"... Abstract — In this paper, we explore bilinear models for jointly addressing 3D face and facial expression recognition. An elas-tically deformable model algorithm that establishes correspon-dence among a set of faces is proposed first and then bilinear models that decouple the identity and facial exp ..."
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Cited by 22 (1 self)
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Abstract — In this paper, we explore bilinear models for jointly addressing 3D face and facial expression recognition. An elas-tically deformable model algorithm that establishes correspon-dence among a set of faces is proposed first and then bilinear models that decouple the identity and facial expression factors are constructed. Fitting these models to unknown faces enables us to perform face recognition invariant to facial expressions and facial expression recognition with unknown identity. A quantitative evaluation of the proposed technique is conducted on the publicly available BU-3DFE face database in comparison with Wang et al.’s work [1] on facial expression recognition and our previous work [2] on face recognition. Experimental results demonstrate an overall 90.5 % facial expression recognition rate and an 86% rank-1 face recognition rate. Index Terms — 3D facial expression recognition, 3D face recog-nition, elastically deformable model, bilinear model. I.
