Results 1 - 10 of 38

Three-Dimensional Face Recognition

by Alexander M. Bronstein, Michael M. Bronstein, Ron Kimmel , 2005
"... An expression-invariant 3D face recognition approach is presented. Our basic assumption is that facial expressions can be modelled as isometries of the facial surface. This allows to construct expression-invariant representations of faces using the bending-invariant canonical forms approach. The re ..."
Abstract - Cited by 150 (24 self) - Add to MetaCart
An expression-invariant 3D face recognition approach is presented. Our basic assumption is that facial expressions can be modelled as isometries of the facial surface. This allows to construct expression-invariant representations of faces using the bending-invariant canonical forms approach. The result is an efficient and accurate face recognition algorithm, robust to facial expressions, that can distinguish between identical twins (the first two authors). We demonstrate a prototype system based on the proposed algorithm and compare its performance to classical face recognition methods. The numerical methods employed by our approach do not require the facial surface explicitly. The surface gradients field, or the surface metric, are sufficient for constructing the expression-invariant representation of any given face. It allows us to perform the 3D face recognition task while avoiding the surface reconstruction stage.

A survey of approaches and challenges in 3d and multi-modal 3d+2d face recognition,

by Kevin W Bowyer , Kyong Chang , Patrick Flynn - 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 ..."
Abstract - Cited by 141 (8 self) - Add to MetaCart
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.
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Rank-based Decision Fusion for 3D Shape-based Face Recognition

by Berk Gökberk, Albert Ali Salah, Lale Akarun - LNCS 3546: INTERNATIONAL CONFERENCE ON AUDIO- AND VIDEO-BASED BIOMETRIC PERSON AUTHENTICATION (AVBPA 2005 , 2005
"... In 3D face recognition systems, 3D facial shape information plays an important role. Various shape representations have been proposed in the literature. The most popular techniques are based on point clouds, surface normals, facial profiles, and statistical analysis of depth images. The contribu ..."
Abstract - Cited by 27 (10 self) - Add to MetaCart
In 3D face recognition systems, 3D facial shape information plays an important role. Various shape representations have been proposed in the literature. The most popular techniques are based on point clouds, surface normals, facial profiles, and statistical analysis of depth images. The contribution of the presented work can be divided into two parts: In the first part, we have developed face classifiers which use these popular techniques. A comprehensive comparison of these representation methods are given using 3D RMA dataset. Experimental results show that the linear discriminant analysis-based representation of depth images and point cloud representation perform best. In the second part of the paper, two different multiple-classifier architectures are developed to fuse individual shape-based face recognizers in parallel and hierarchical fashions at the decision level. It is shown that a significant performance improvement is possible when using rank-based decision fusion in ensemble methods.

3D face authentication and recognition based on bilateral symmetry analysis

by Liyan Zhang, Charles Lockwood - in Education from King's College London in 1995 and 1996 respectively. From 1996 until , 2006
"... We present a novel and computationally fast method for automatic human face authentication. Taking a 3D triangular facial mesh as input, the approach first automatically extracts the bilateral symmetry plane of the face surface. The intersection between the symmetry plane and the facial surface, nam ..."
Abstract - Cited by 16 (1 self) - Add to MetaCart
We present a novel and computationally fast method for automatic human face authentication. Taking a 3D triangular facial mesh as input, the approach first automatically extracts the bilateral symmetry plane of the face surface. The intersection between the symmetry plane and the facial surface, namely the Symmetry Profile, is then computed. By using both the mean curvature plot of the facial surface and the curvature plot of the symmetry profile curve, three essential points of the nose on the symmetry profile are automatically extracted. The three essential points uniquely determine a Face Intrinsic Coordinate System (FICS). Different faces are aligned based on the FICS. The Symmetry Profile, together with two transverse profiles compose a compact representation, called the SFC representation, of a 3D face surface. The face authentication and recognition steps are finally performed by comparing the SFC representations of the faces. The proposed method was tested on 213 face surfaces, which come from 164 individuals and cover a wide ethnic and age variety and variable facial expressions. The Equal Error Rate (EER) of face authentication on the tested faces is 3.7%; the rank one recognition rate is 90%.
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A Mosaicing Scheme for Pose-Invariant Face Recognition

by Richa Singh, Student Member, Mayank Vatsa, Student Member, Arun Ross, Afzel Noore
"... Abstract—Mosaicing entails the consolidation of information represented by multiple images through the application of a registration and blending procedure. We describe a face mosaicing scheme that generates a composite face image during enrollment based on the evidence provided by frontal and semip ..."
Abstract - Cited by 13 (2 self) - Add to MetaCart
Abstract—Mosaicing entails the consolidation of information represented by multiple images through the application of a registration and blending procedure. We describe a face mosaicing scheme that generates a composite face image during enrollment based on the evidence provided by frontal and semiprofile face images of an individual. Face mosaicing obviates the need to store multiple face templates representing multiple poses of a user’s face image. In the proposed scheme, the side profile images are aligned with the frontal image using a hierarchical registration algorithm that exploits neighborhood properties to determine the transformation relating the two images. Multiresolution splining is then used to blend the side profiles with the frontal image, thereby generating a composite face image of the user. A texture-based face recognition technique that is a slightly modified version of the C2 algorithm proposed by Serre et al. is used to compare a probe face image with the gallery face mosaic. Experiments conducted on three different databases indicate that face mosaicing, as described in this paper, offers significant benefits by accounting for the pose variations that are commonly observed in face images. Index Terms—Face mosaicing, face recognition, multiresolution splines, mutual information. I.
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Learning to Fuse 3D+2D Based Face Recognition at Both Feature and Decision Levels

by Stan Z. Li, Chunshui Zhao, Meng Ao, Zhen Lei
"... 2D intensity images and 3D shape models are both useful for face recognition, but in different ways. While algorithms have long been developed using 2D or 3D data, recently has seen work on combining both into multi-modal face biometrics to achieve higher performance. However, the fusion of the two ..."
Abstract - Cited by 13 (1 self) - Add to MetaCart
2D intensity images and 3D shape models are both useful for face recognition, but in different ways. While algorithms have long been developed using 2D or 3D data, recently has seen work on combining both into multi-modal face biometrics to achieve higher performance. However, the fusion of the two modalities has mostly been at the decision level, based on scores obtained from independent 2D and 3D matchers. In this paper, we propose a systematic framework for fusing 2D and 3D face recognition at both feature and decision levels, by exploring synergies of the two modalities at these levels. The novelties are the following. First, we propose to use Local Binary Pattern (LBP) features to represent 3D faces and present a statistical learning procedure for feature selection and classifier learning. This leads to a matching engine for 3D face recognition. Second, we propose a statistical learning approach for fusing 2D and 3D based face recognition at both feature and decision levels. Experiments show that the fusion at both levels yields significantly better performance than fusion at the decision level. 1
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Highly accurate and fast face recognition using near infrared images

by Stan Z. Li, Rufeng Chu, Meng Ao, Lun Zhang, Ran He - in Proc. IAPR Int. Conf. Biometrics , 2006
"... Abstract. In this paper, we present a highly accurate, realtime face recognition system for cooperative user applications. The novelties are: (1) a novel design of camera hardware, and (2) a learning based procedure for effective face and eye detection and recognition with the resulting imagery. The ..."
Abstract - Cited by 12 (3 self) - Add to MetaCart
Abstract. In this paper, we present a highly accurate, realtime face recognition system for cooperative user applications. The novelties are: (1) a novel design of camera hardware, and (2) a learning based procedure for effective face and eye detection and recognition with the resulting imagery. The hardware minimizes environmental lighting and delivers face images with frontal lighting. This avoids many problems in subsequent face processing to a great extent. The face detection and recognition algorithms are based on a local feature representation. Statistical learning is applied to learn most effective features and classifiers for building face detection and recognition engines. The novel imaging system and the detection and recognition engines are integrated into a powerful face recognition system. Evaluated in real-world user scenario, a condition that is harder than a technology evaluation such as Face Recognition Vendor Tests (FRVT), the system has demonstrated excellent accuracy, speed and usability. 1
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Partial face matching between near infrared and visual images

by Dong Yi, Shengcai Liao, Zhen Lei, Jitao Sang, Stan Z. Li - in MBGC portal challenge,” in Proceedings of the 3rd IAPR/IEEE International Conference on Biometrics , 2009
"... Abstract. The latest multi-biometric grand challenge (MBGC 2008) sets up a new experiment in which near infrared (NIR) face videos containing partial faces are used as a probe set and the visual (VIS) images of full faces are used as the target set. This is challenging for two reasons: (1) it has to ..."
Abstract - Cited by 9 (3 self) - Add to MetaCart
Abstract. The latest multi-biometric grand challenge (MBGC 2008) sets up a new experiment in which near infrared (NIR) face videos containing partial faces are used as a probe set and the visual (VIS) images of full faces are used as the target set. This is challenging for two reasons: (1) it has to deal with partially occluded faces in the NIR videos, and (2) the matching is between heterogeneous NIR and VIS faces. Partial face matching is also a problem often confronted in many video based face biometric applications. In this paper, we propose a novel approach for solving this challenging prob-lem. For partial face matching, we propose a local patch based method to deal with partial face data. For heterogeneous face matching, we propose the philoso-phy of enhancing common features in heterogeneous images while reducing dif-ferences. This is realized by using edge-enhancing filters, which at the same time is also beneficial for partial face matching. The approach requires neither learn-ing procedures nor training data. Experiments are performed using the MBGC portal challenge data, comparing with several known state-of-the-arts methods. Extensive results show that the proposed approach, without knowing statistical characteristics of the subjects or data, outperforms the methods of contrast sig-nificantly, with ten-fold higher verification rates at FAR of 0.1%.
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Performance enhancement of 2D face recognition via mosaicing

by Richa Singh, Mayank Vatsa, Arun Ross, Afzel Noore - in Proc. 4th IEEE Workshop Autom. Identification Adv. Technol., 2005 , 2005
"... We describe a face mosaicing scheme that generates a composite face image during enrollment based on the evidence provided by frontal and semi-profile face images of an individual. Face mosaicing obviates the need to store multiple face templates representing multiple poses of a user’s face image. I ..."
Abstract - Cited by 9 (4 self) - Add to MetaCart
We describe a face mosaicing scheme that generates a composite face image during enrollment based on the evidence provided by frontal and semi-profile face images of an individual. Face mosaicing obviates the need to store multiple face templates representing multiple poses of a user’s face image. In the proposed scheme, the side profile images are aligned with the frontal image using a terrain transform that exploits neighborhood properties to determine the transformation relating the 2 images. Multiresolution splining is then used to blend the side profiles with the frontal image thereby generating a composite face image of the user. A local binary pattern matching algorithm is used to compare an input face image with the template face mosaic. Experiments conducted on a small dataset of 27 users indicate that face mosaicing as described in this paper offers significant benefits by accounting for the pose variations commonly observed in face images. 1.
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A near-infrared image based face recognition system

by Stan Z. Li, Lun Zhang, Shengcai Liao, Xiangxin Zhu, Rufeng Chu, Meng Ao, Ran He - in [Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition , 2006
"... In this paper, we present a near infrared (NIR) image based face recognition system. Firstly, we describe a design of NIR image capture device which minimizes influence of environmental lighting on face images. Both face and facial feature localization and face recognition are performed using local ..."
Abstract - Cited by 6 (2 self) - Add to MetaCart
In this paper, we present a near infrared (NIR) image based face recognition system. Firstly, we describe a design of NIR image capture device which minimizes influence of environmental lighting on face images. Both face and facial feature localization and face recognition are performed using local features with AdaBoost learning. An evaluation in real-world user scenario shows that the system achieves excellent accuracy, speed and usability. 1.
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