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A survey of approaches and challenges in 3d and multi-modal 3d+2d face recognition,
- Comp. Vis. and Imag. Understand.
, 2006
"... Abstract This survey focuses on recognition performed by matching models of the three-dimensional shape of the face, either alone or in combination with matching corresponding two-dimensional intensity images. Research trends to date are summarized, and challenges confronting the development of mor ..."
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Cited by 141 (8 self)
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Abstract This survey focuses on recognition performed by matching models of the three-dimensional shape of the face, either alone or in combination with matching corresponding two-dimensional intensity images. Research trends to date are summarized, and challenges confronting the development of more accurate three-dimensional face recognition are identified. These challenges include the need for better sensors, improved recognition algorithms, and more rigorous experimental methodology.
Robust 3D Face Recognition by Local Shape Difference Boosting
, 2010
"... This paper proposes a new 3D face recognition approach, Collective Shape Difference Classifier (CSDC), to meet practical application requirements, i.e., high recognition performance, high computational efficiency, and easy implementation. We first present a fast posture alignment method which is sel ..."
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Cited by 16 (3 self)
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This paper proposes a new 3D face recognition approach, Collective Shape Difference Classifier (CSDC), to meet practical application requirements, i.e., high recognition performance, high computational efficiency, and easy implementation. We first present a fast posture alignment method which is self-dependent and avoids the registration between an input face against every face in the gallery. Then, a Signed Shape Difference Map (SSDM) is computed between two aligned 3D faces as a mediate representation for the shape comparison. Based on the SSDMs, three kinds of features are used to encode both the local similarity and the change characteristics between facial shapes. The most discriminative local features are selected optimally by boosting and trained as weak classifiers for assembling three collective strong classifiers, namely, CSDCs with respect to the three kinds of features. Different schemes are designed for verification and identification to pursue high performance in both recognition and computation. The experiments, carried out on FRGC v2 with the standard protocol, yield three verification rates all better than 97.9 percent with the FAR of 0.1 percent and rank-1 recognition rates above 98 percent. Each recognition against a gallery with 1,000 faces only takes about 3.6 seconds. These experimental results demonstrate that our algorithm is not only effective but also time efficient.
A 3D Face Matching Framework
- In Shape Modeling and Applications (SMI
, 2008
"... Many 3D face matching techniques have been developed to perform face recognition. Among these techniques are variants of 3D facial curve matching, which are techniques that reduce the amount of face data to one or a few 3D curves. The face’s central profile, for instance, proved to work well. Howeve ..."
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Cited by 13 (3 self)
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Many 3D face matching techniques have been developed to perform face recognition. Among these techniques are variants of 3D facial curve matching, which are techniques that reduce the amount of face data to one or a few 3D curves. The face’s central profile, for instance, proved to work well. However, the selection of the optimal set of 3D curves and the best way to match them is still under-exposed. We propose a 3D face matching framework that allows profile and contour based face matching. Using this framework we evaluate profile and contour types including those described in literature, and select subsets of facial curves for effective and efficient face matching. Results on the 3D face retrieval track of SHREC’07 (the 3D SHape Retrieval Contest) shows the highest mean average precision achieved so far, using only eight facial curves of 45 samples each. 1
2007, ‘Adapting Geometric Attributes for Expression-Invariant 3D Face Recognition
- In: IEEE Conference on Shape Modeling and Applications
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Advances and Challenges in 3D and 2D+3D Human Face Recognition
"... Automated human face recognition is required in numerous applications. While considerable progress has been made in color/two dimensional (2D) face recognition, three dimensional (3D) face recognition technology is much less developed. 3D face recognition approaches based on the appearance of range ..."
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Cited by 5 (1 self)
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Automated human face recognition is required in numerous applications. While considerable progress has been made in color/two dimensional (2D) face recognition, three dimensional (3D) face recognition technology is much less developed. 3D face recognition approaches based on the appearance of range images and geometric properties of the facial surface have been proposed. Methods that combine 2D and 3D modalities also exist. These innovations have advanced the field and have created novel areas of investigation. The purpose of this chapter is to provide a summary and critical analysis of the progress in 3D and 2D+3D face recognition. The chapter also identifies open problems and directions for future work in the area. 2
3-D Face Recognition Under Occlusion Using Masked Projection
"... Abstract—With advances in sensor technology, the three-dimensional (3-D) face has become an emerging biometric modality, preferred especially in high security applications. However, dealing with occlusions covering the facial surface is a great challenge, which should be handled to enable applicabil ..."
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Abstract—With advances in sensor technology, the three-dimensional (3-D) face has become an emerging biometric modality, preferred especially in high security applications. However, dealing with occlusions covering the facial surface is a great challenge, which should be handled to enable applicability to fully automatic security systems. In this paper, we propose a fully automatic 3-D face recognition system which is robust to occlusions. We basically consider two problems: 1) occlusion handling for surface registration, and 2) missing data handling for classification based on subspace analysis techniques. For the alignment problem, we employ an adaptively-selected-model-based registration scheme, where a face model is selected for an occluded face such that only the valid nonoccluded patches are utilized. After registering to the model, occlusions are detected and removed. In the classification stage, a masking strategy, which we call masked projection, isproposed to enable the use of subspace analysis techniques with incomplete data. Furthermore, a regional scheme suitable for occlusion handling is incorporated in classification to improve the overall results. Experimental results on two databases with realistic facial occlusions, namely, the Bosphorus and the UMB-DB, are reported. Experimental results confirm that registration based on the adaptively selected model together with the masked subspace analysis classification offer an occlusion robust face recognition system. Index Terms—3-D face recognition, 3-D registration, biometrics, curvature descriptors. I.
3D Shape Registration
"... Registration is the problem of bringing together two or more 3D shapes, either of the same object or of two different but similar objects. This chapter first introduces the classical Iterative Closest Point (ICP) algorithm which represents the gold standard registration method. Current limitations o ..."
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Registration is the problem of bringing together two or more 3D shapes, either of the same object or of two different but similar objects. This chapter first introduces the classical Iterative Closest Point (ICP) algorithm which represents the gold standard registration method. Current limitations of ICP are addressed and the most popular variants of ICP are described to improve the basic implementation in several ways. Challenging registration scenarios are analyzed and a taxonomy of recent and promising alternative registration techniques is introduced. Three case studies are then described with an increasing level of difficulty. The first case study describes a simple but effective technique to detect outliers. The second case study uses the Levenberg-Marquardt optimization procedure to solve standard pairwise registration. The third case study focuses on the challenging problem of deformable object registration. Finally, open issues and directions for future work are discussed and conclusions are drawn. 1
Introduction to Computer Vision from Automatic Face Analysis Viewpoint
, 2008
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Oriented Gradient Maps based Automatic Asymmetric 3D-2D Face Recognition
"... Recently, asymmetric 3D-2D face recognition has been paid increasing attention. It enrolls in textured 3D faces and performs identification using only 2D facial images, there-fore it generally achieves a better result than 2D algorithms do, and avoids inconvenience of data acquisition and com-putati ..."
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Recently, asymmetric 3D-2D face recognition has been paid increasing attention. It enrolls in textured 3D faces and performs identification using only 2D facial images, there-fore it generally achieves a better result than 2D algorithms do, and avoids inconvenience of data acquisition and com-putation of 3D methods as well. In this paper, a biological vision-based facial representation, namely Oriented Gradi-ent Maps (OGMs), is introduced for such an application. It simulates the response of complex neurons to gradient in-formation within a pre-defined neighborhood, and thus can describe local texture changes of 2D faces and local geome-try variations of 3D faces at the same time. Due to its prop-erty of being highly distinctive, these OGMs improve accu-racies of both matching steps of asymmetric face recogni-tion, i.e. (1) 3D-2D matching using Canonical Correlation Analysis (CCA); (2) 2D-2D matching using LBP histogram based features and Sparse Representation Classifier (SRC). Some comparative experiments are carried out on the com-plete FRGC v2.0 database, and the achieved results clearly highlight the effectiveness of the biological vision-based fa-cial description and its successful application to asymmet-ric face recognition. 1.
