Pub Date : 2011-10-11DOI: 10.1109/IJCB.2011.6117601
Khoa Luu, Keshav Seshadri, M. Savvides, T. D. Bui, C. Suen
In this paper we propose a novel Contourlet Appearance Model (CAM) that is more accurate and faster at localizing facial landmarks than Active Appearance Models (AAMs). Our CAM also has the ability to not only extract holistic texture information, as AAMs do, but can also extract local texture information using the Nonsubsampled Contourlet Transform (NSCT). We demonstrate the efficiency of our method by applying it to the problem of facial age estimation. Compared to previously published age estimation techniques, our approach yields more accurate results when tested on various face aging databases.
{"title":"Contourlet appearance model for facial age estimation","authors":"Khoa Luu, Keshav Seshadri, M. Savvides, T. D. Bui, C. Suen","doi":"10.1109/IJCB.2011.6117601","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117601","url":null,"abstract":"In this paper we propose a novel Contourlet Appearance Model (CAM) that is more accurate and faster at localizing facial landmarks than Active Appearance Models (AAMs). Our CAM also has the ability to not only extract holistic texture information, as AAMs do, but can also extract local texture information using the Nonsubsampled Contourlet Transform (NSCT). We demonstrate the efficiency of our method by applying it to the problem of facial age estimation. Compared to previously published age estimation techniques, our approach yields more accurate results when tested on various face aging databases.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124980287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-11DOI: 10.1109/IJCB.2011.6117486
Hyun-ju Maeng, Hyun-Cheol Choi, U. Park, Seong-Whan Lee, Anil K. Jain
Face recognition at a distance is gaining wide attention in order to augment the surveillance systems with face recognition capability. However, face recognition at a distance in nighttime has not yet received adequate attention considering the increased security threats at nighttime. We introduce a new face image database, called Near-Infrared Face Recognition at a Distance Database (NFRAD-DB). Images in NFRAD-DB are collected at a distance of up to 60 meters with 50 different subjects using a near-infrared camera, a telescope, and near-infrared illuminator. We provide face recognition performance using FaceVACS, DoG-SIFT, and DoG-MLBP representations. The face recognition test consisted of NIR images of these 50 subjects at 60 meters as probe and visible images at 1 meter with additional mug shot images of 10,000 subjects as gallery. Rank-1 identification accuracy of 28 percent was achieved from the proposed method compared to 18 percent rank-1 accuracy of a state of the art face recognition system, FaceVACS. These recognition results are encouraging given this challenging matching problem due to the illumination pattern and insufficient brightness in NFRAD images.
{"title":"NFRAD: Near-Infrared Face Recognition at a Distance","authors":"Hyun-ju Maeng, Hyun-Cheol Choi, U. Park, Seong-Whan Lee, Anil K. Jain","doi":"10.1109/IJCB.2011.6117486","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117486","url":null,"abstract":"Face recognition at a distance is gaining wide attention in order to augment the surveillance systems with face recognition capability. However, face recognition at a distance in nighttime has not yet received adequate attention considering the increased security threats at nighttime. We introduce a new face image database, called Near-Infrared Face Recognition at a Distance Database (NFRAD-DB). Images in NFRAD-DB are collected at a distance of up to 60 meters with 50 different subjects using a near-infrared camera, a telescope, and near-infrared illuminator. We provide face recognition performance using FaceVACS, DoG-SIFT, and DoG-MLBP representations. The face recognition test consisted of NIR images of these 50 subjects at 60 meters as probe and visible images at 1 meter with additional mug shot images of 10,000 subjects as gallery. Rank-1 identification accuracy of 28 percent was achieved from the proposed method compared to 18 percent rank-1 accuracy of a state of the art face recognition system, FaceVACS. These recognition results are encouraging given this challenging matching problem due to the illumination pattern and insufficient brightness in NFRAD images.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124582993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-11DOI: 10.1109/IJCB.2011.6117478
G. Chiachia, A. Falcão, A. Rocha
Most face recognition methods rely on a common feature space to represent the faces, in which the face aspects that better distinguish among all the persons are emphasized. This strategy may be inadequate to represent more appropriate aspects of a specific person's face, since there may be some aspects that are good at distinguishing only a given person from the others. Based on this idea and supported by some findings in the human perception of faces, we propose a face recognition framework that associates a feature space to each person that we intend to recognize. Such feature spaces are conceived to underline the discriminating face aspects of the persons they represent. In order to recognize a probe, we match it to the gallery in all the feature spaces and fuse the results to establish the identity. With the help of an algorithm that we devise, the Discriminant Patch Selection, we were capable of carrying out experiments to intuitively compare the traditional approaches with the person-specific representation. In the performed experiments, the person-specific face representation always resulted in a better identification of the faces.
{"title":"Person-specific face representation for recognition","authors":"G. Chiachia, A. Falcão, A. Rocha","doi":"10.1109/IJCB.2011.6117478","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117478","url":null,"abstract":"Most face recognition methods rely on a common feature space to represent the faces, in which the face aspects that better distinguish among all the persons are emphasized. This strategy may be inadequate to represent more appropriate aspects of a specific person's face, since there may be some aspects that are good at distinguishing only a given person from the others. Based on this idea and supported by some findings in the human perception of faces, we propose a face recognition framework that associates a feature space to each person that we intend to recognize. Such feature spaces are conceived to underline the discriminating face aspects of the persons they represent. In order to recognize a probe, we match it to the gallery in all the feature spaces and fuse the results to establish the identity. With the help of an algorithm that we devise, the Discriminant Patch Selection, we were capable of carrying out experiments to intuitively compare the traditional approaches with the person-specific representation. In the performed experiments, the person-specific face representation always resulted in a better identification of the faces.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123754087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-11DOI: 10.1109/IJCB.2011.6117583
Junjie Yan, Zhen Lei, Dong Yi, S. Li
Linear discriminant analysis with nearest neighborhood classifier (LDA + NN) has been commonly used in face recognition, but it often confronts with two problems in real applications: (1) it cannot incrementally deal with the information of training instances; (2) it cannot achieve fast search against large scale gallery set. In this paper, we use incremental LDA (ILDA) and hashing based search method to deal with these two problems. Firstly two incremental LDA algorithms are proposed under spectral regression framework, namely exact incremental spectral regression discriminant analysis (EI-SRDA) and approximate incremental spectral regression discriminant analysis (AI-SRDA). Secondly we propose a similarity hashing algorithm of sub-linear complexity to achieve quick recognition from large gallery set. Experiments on FRGC and self-collected 100,000 faces database show the effective of our methods.
{"title":"Towards incremental and large scale face recognition","authors":"Junjie Yan, Zhen Lei, Dong Yi, S. Li","doi":"10.1109/IJCB.2011.6117583","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117583","url":null,"abstract":"Linear discriminant analysis with nearest neighborhood classifier (LDA + NN) has been commonly used in face recognition, but it often confronts with two problems in real applications: (1) it cannot incrementally deal with the information of training instances; (2) it cannot achieve fast search against large scale gallery set. In this paper, we use incremental LDA (ILDA) and hashing based search method to deal with these two problems. Firstly two incremental LDA algorithms are proposed under spectral regression framework, namely exact incremental spectral regression discriminant analysis (EI-SRDA) and approximate incremental spectral regression discriminant analysis (AI-SRDA). Secondly we propose a similarity hashing algorithm of sub-linear complexity to achieve quick recognition from large gallery set. Experiments on FRGC and self-collected 100,000 faces database show the effective of our methods.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123623849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-11DOI: 10.1109/IJCB.2011.6117505
X. Yang, Jianjiang Feng, Jie Zhou
In recent years, law enforcement agencies are increasingly using palmprint to identify criminals. For law enforcement palmprint identification systems, efficiency is a very important but challenging problem because of large database size and poor image quality. Existing palmprint identification systems are not sufficiently fast for practical applications. To solve this problem, a novel palmprint indexing algorithm based on ridge features is proposed in this paper. A palmprint is pre-aligned by registering its orientation field with respect to a set of reference orientation fields, which are obtained by clustering training palmprint orientation fields. Indexing is based on comparing ridge orientation fields and ridge density maps, which is much faster than minutiae matching. Proposed algorithm achieved an error rate of 1% at a penetration rate of 2.25% on a palmprint database consisting of 13,416 palmprints. Searching a query palmprint over the whole database takes only 0.22 seconds.
{"title":"Palmprint indexing based on ridge features","authors":"X. Yang, Jianjiang Feng, Jie Zhou","doi":"10.1109/IJCB.2011.6117505","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117505","url":null,"abstract":"In recent years, law enforcement agencies are increasingly using palmprint to identify criminals. For law enforcement palmprint identification systems, efficiency is a very important but challenging problem because of large database size and poor image quality. Existing palmprint identification systems are not sufficiently fast for practical applications. To solve this problem, a novel palmprint indexing algorithm based on ridge features is proposed in this paper. A palmprint is pre-aligned by registering its orientation field with respect to a set of reference orientation fields, which are obtained by clustering training palmprint orientation fields. Indexing is based on comparing ridge orientation fields and ridge density maps, which is much faster than minutiae matching. Proposed algorithm achieved an error rate of 1% at a penetration rate of 2.25% on a palmprint database consisting of 13,416 palmprints. Searching a query palmprint over the whole database takes only 0.22 seconds.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127627628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-11DOI: 10.1109/IJCB.2011.6117498
Huimin Guo, W. R. Schwartz, L. Davis
We present a method for face verification that combines Partial Least Squares (PLS) and the One-Shot similarity model[28]. First, a large feature set combining shape, texture and color information is used to describe a face. Then PLS is applied to reduce the dimensionality of the feature set with multi-channel feature weighting. This provides a discriminative facial descriptor. PLS regression is used to compute the similarity score of an image pair by One-Shot learning. Given two feature vector representing face images, the One-Shot algorithm learns discriminative models exclusively for the vectors being compared. A small set of unlabeled images, not containing images belonging to the people being compared, is used as a reference (negative) set. The approach is evaluated on the Labeled Face in the Wild (LFW) benchmark and shows very comparable results to the state-of-the-art methods (achieving 86.12% classification accuracy) while maintaining simplicity and good generalization ability.
我们提出了一种结合偏最小二乘(PLS)和一次性相似性模型的人脸验证方法[28]。首先，利用结合形状、纹理和颜色信息的大型特征集对人脸进行描述。然后利用PLS对特征集进行多通道特征加权降维。这提供了一个判别性的面部描述符。采用单次学习的方法，利用PLS回归计算图像对的相似度得分。给定两个代表人脸图像的特征向量，One-Shot算法专门为被比较的向量学习判别模型。一小组未标记的图像，不包含属于被比较的人的图像，被用作参考(否定)集。该方法在Labeled Face in The Wild (LFW)基准上进行了评估，显示出与最先进的方法非常相似的结果(达到86.12%的分类准确率)，同时保持了简单性和良好的泛化能力。
{"title":"Face verification using large feature sets and one shot similarity","authors":"Huimin Guo, W. R. Schwartz, L. Davis","doi":"10.1109/IJCB.2011.6117498","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117498","url":null,"abstract":"We present a method for face verification that combines Partial Least Squares (PLS) and the One-Shot similarity model[28]. First, a large feature set combining shape, texture and color information is used to describe a face. Then PLS is applied to reduce the dimensionality of the feature set with multi-channel feature weighting. This provides a discriminative facial descriptor. PLS regression is used to compute the similarity score of an image pair by One-Shot learning. Given two feature vector representing face images, the One-Shot algorithm learns discriminative models exclusively for the vectors being compared. A small set of unlabeled images, not containing images belonging to the people being compared, is used as a reference (negative) set. The approach is evaluated on the Labeled Face in the Wild (LFW) benchmark and shows very comparable results to the state-of-the-art methods (achieving 86.12% classification accuracy) while maintaining simplicity and good generalization ability.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127795950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-11DOI: 10.1109/IJCB.2011.6117522
Roberto Tronci, Daniele Muntoni, Gianluca Fadda, Maurizio Pili, Nicola Sirena, G. Murgia, Marco Ristori, Sardegna Ricerche, F. Roli
We faced the problem of detecting 2-D face spoofing attacks performed by placing a printed photo of a real user in front of the camera. For this type of attack it is not possible to relay just on the face movements as a clue of vitality because the attacker can easily simulate such a case, and also because real users often show a “low vitality” during the authentication session. In this paper, we perform both video and static analysis in order to employ complementary information about motion, texture and liveness and consequently to obtain a more robust classification.
{"title":"Fusion of multiple clues for photo-attack detection in face recognition systems","authors":"Roberto Tronci, Daniele Muntoni, Gianluca Fadda, Maurizio Pili, Nicola Sirena, G. Murgia, Marco Ristori, Sardegna Ricerche, F. Roli","doi":"10.1109/IJCB.2011.6117522","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117522","url":null,"abstract":"We faced the problem of detecting 2-D face spoofing attacks performed by placing a printed photo of a real user in front of the camera. For this type of attack it is not possible to relay just on the face movements as a clue of vitality because the attacker can easily simulate such a case, and also because real users often show a “low vitality” during the authentication session. In this paper, we perform both video and static analysis in order to employ complementary information about motion, texture and liveness and consequently to obtain a more robust classification.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126818991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-11DOI: 10.1109/IJCB.2011.6117550
Hee-seung Choi, Abhishek Nagar, Anil K. Jain
Fingerprint evidence is routinely used by forensics and law enforcement agencies worldwide to apprehend and convict criminals, a practice in use for over 100 years. The use of fingerprints has been accepted as an infallible proof of identity based on two premises: (i) permanence or persistence, and (ii) uniqueness or individuality. However, in the absence of any theoretical results that establish the uniqueness or individuality of fingerprints, the use of fingerprints in various court proceedings is being questioned. This has raised awareness in the forensics community about the need to quantify the evidential value of fingerprint matching. A few studies that have studied this problem estimate this evidential value in one of two ways: (i) feature modeling, where a statistical (generative) model for fingerprint features, primarily minutiae, is developed which is then used to estimate the matching error and (ii) match score modeling, where a set of match scores obtained over a database is used to estimate the matching error rates. Our focus here is on match score modeling and we develop metrics to evaluate the effectiveness and reliability of the proposed evidential measure. Compared to previous approaches, the proposed measure allows explicit utilization of prior odds. Further, we also incorporate fingerprint image quality to improve the reliability of the estimated evidential value.
{"title":"On the evidential value of fingerprints","authors":"Hee-seung Choi, Abhishek Nagar, Anil K. Jain","doi":"10.1109/IJCB.2011.6117550","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117550","url":null,"abstract":"Fingerprint evidence is routinely used by forensics and law enforcement agencies worldwide to apprehend and convict criminals, a practice in use for over 100 years. The use of fingerprints has been accepted as an infallible proof of identity based on two premises: (i) permanence or persistence, and (ii) uniqueness or individuality. However, in the absence of any theoretical results that establish the uniqueness or individuality of fingerprints, the use of fingerprints in various court proceedings is being questioned. This has raised awareness in the forensics community about the need to quantify the evidential value of fingerprint matching. A few studies that have studied this problem estimate this evidential value in one of two ways: (i) feature modeling, where a statistical (generative) model for fingerprint features, primarily minutiae, is developed which is then used to estimate the matching error and (ii) match score modeling, where a set of match scores obtained over a database is used to estimate the matching error rates. Our focus here is on match score modeling and we develop metrics to evaluate the effectiveness and reliability of the proposed evidential measure. Compared to previous approaches, the proposed measure allows explicit utilization of prior odds. Further, we also incorporate fingerprint image quality to improve the reliability of the estimated evidential value.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126033705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-11DOI: 10.1109/IJCB.2011.6117804
Leila Mirmohamadsadeghi, A. Drygajlo
Palm vein feature extraction from near infrared images is a challenging problem in hand pattern recognition. In this paper, a promising new approach based on local texture patterns is proposed. First, operators and histograms of multi-scale Local Binary Patterns (LBPs) are investigated in order to identify new efficient descriptors for palm vein patterns. Novel higher-order local pattern descriptors based on Local Derivative Pattern (LDP) histograms are then investigated for palm vein description. Both feature extraction methods are compared and evaluated in the framework of verification and identification tasks. Extensive experiments on CASIA Multi-Spectral Palmprint Image Database V1.0 (CASIA database) identify the LBP and LDP descriptors which are better adapted to palm vein texture. Tests on the CASIA datasets also show that the best adapted LDP descriptors consistently outperform their LBP counterparts in both palm vein verification and identification.
{"title":"Palm vein recognition with Local Binary Patterns and Local Derivative Patterns","authors":"Leila Mirmohamadsadeghi, A. Drygajlo","doi":"10.1109/IJCB.2011.6117804","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117804","url":null,"abstract":"Palm vein feature extraction from near infrared images is a challenging problem in hand pattern recognition. In this paper, a promising new approach based on local texture patterns is proposed. First, operators and histograms of multi-scale Local Binary Patterns (LBPs) are investigated in order to identify new efficient descriptors for palm vein patterns. Novel higher-order local pattern descriptors based on Local Derivative Pattern (LDP) histograms are then investigated for palm vein description. Both feature extraction methods are compared and evaluated in the framework of verification and identification tasks. Extensive experiments on CASIA Multi-Spectral Palmprint Image Database V1.0 (CASIA database) identify the LBP and LDP descriptors which are better adapted to palm vein texture. Tests on the CASIA datasets also show that the best adapted LDP descriptors consistently outperform their LBP counterparts in both palm vein verification and identification.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125906506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-11DOI: 10.1109/IJCB.2011.6117472
J. Heo, M. Savvides
Generic 3D face pose estimation from a single 2D facial image is an extremely crucial requirement for face-related research areas. To meet with the remaining challenges for face pose estimation, suggested Murphy-Chutorian et al. [13], we believe that the first step is to create a large corpus of a 3D facial shape database in which the statistical relationship between projected 2D shapes and corresponding pose parameters can be easily observed. Because facial geometry provides the most essential information for facial pose, understanding the effect of pose parameters in 2D facial shapes is a key step toward solving the remaining challenges. In this paper, we present necessary tasks to reconstruct 3D facial shapes from multiple 2D images and then explain how to generate 2D projected shapes at any rotation interval. To deal with self occlusions, a novel hidden points removal (HPR) algorithm is also proposed. By flexibly changing the number of points in 2D shapes, we evaluate the performance of two different approaches for achieving generic 3D pose estimation in both coarse and fine levels and analyze the importance of facial shapes toward generic 3D pose estimation.
从单个二维人脸图像中估计通用的三维人脸姿态是人脸相关研究领域的一个极其重要的要求。Murphy-Chutorian et al.[13]建议，为了应对面部姿态估计的剩余挑战，我们认为第一步是创建一个大型的3D面部形状数据库语料库，其中可以很容易地观察到投影的2D形状与相应姿态参数之间的统计关系。由于面部几何为面部姿态提供了最基本的信息，因此了解姿态参数在二维面部形状中的影响是解决其余挑战的关键一步。在本文中，我们提出了从多个二维图像重建三维面部形状的必要任务，然后解释了如何在任何旋转间隔生成二维投影形状。针对自遮挡，提出了一种新的隐点去除(HPR)算法。通过灵活地改变二维形状中的点的数量，我们在粗和细两个层面上评估了两种不同的方法在实现通用三维姿态估计方面的性能，并分析了面部形状对通用三维姿态估计的重要性。
{"title":"Generic 3D face pose estimation using facial shapes","authors":"J. Heo, M. Savvides","doi":"10.1109/IJCB.2011.6117472","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117472","url":null,"abstract":"Generic 3D face pose estimation from a single 2D facial image is an extremely crucial requirement for face-related research areas. To meet with the remaining challenges for face pose estimation, suggested Murphy-Chutorian et al. [13], we believe that the first step is to create a large corpus of a 3D facial shape database in which the statistical relationship between projected 2D shapes and corresponding pose parameters can be easily observed. Because facial geometry provides the most essential information for facial pose, understanding the effect of pose parameters in 2D facial shapes is a key step toward solving the remaining challenges. In this paper, we present necessary tasks to reconstruct 3D facial shapes from multiple 2D images and then explain how to generate 2D projected shapes at any rotation interval. To deal with self occlusions, a novel hidden points removal (HPR) algorithm is also proposed. By flexibly changing the number of points in 2D shapes, we evaluate the performance of two different approaches for achieving generic 3D pose estimation in both coarse and fine levels and analyze the importance of facial shapes toward generic 3D pose estimation.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126849297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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