Pub Date : 2017-10-01DOI: 10.1109/BTAS.2017.8272753
K. Raja, P. Wasnik, Ramachandra Raghavendra, C. Busch
Smartphone based facial biometric systems have been well used in many of the security applications starting from simple phone unlocking to secure banking applications. This work presents a new approach of exploring the intrinsic characteristics of the smartphone camera to capture a number of stack images in the depth-of-field. With the set of stack images obtained, we present a new feature-free and classifier-free approach to provide the presentation attack resistant face biometric system. With the entire system implemented on the smartphone, we demonstrate the applicability of the proposed scheme in obtaining a stack of images with varying focus to effectively determine the presentation attacks. We create a new database of 13250 images at different focal length to present a detailed analysis of vulnerability together with the evaluation of proposed scheme. An extensive evaluation of the newly created database comprising of 5 different Presentation Attack Instruments (PAI) has demonstrated an outstanding performance on all 5 PAI through proposed approach. With the set ofcomplementary benefits of proposed approach illustrated in this work, we deduce the robustness towards unseen 2D attacks.
{"title":"Robust face presentation attack detection on smartphones : An approach based on variable focus","authors":"K. Raja, P. Wasnik, Ramachandra Raghavendra, C. Busch","doi":"10.1109/BTAS.2017.8272753","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272753","url":null,"abstract":"Smartphone based facial biometric systems have been well used in many of the security applications starting from simple phone unlocking to secure banking applications. This work presents a new approach of exploring the intrinsic characteristics of the smartphone camera to capture a number of stack images in the depth-of-field. With the set of stack images obtained, we present a new feature-free and classifier-free approach to provide the presentation attack resistant face biometric system. With the entire system implemented on the smartphone, we demonstrate the applicability of the proposed scheme in obtaining a stack of images with varying focus to effectively determine the presentation attacks. We create a new database of 13250 images at different focal length to present a detailed analysis of vulnerability together with the evaluation of proposed scheme. An extensive evaluation of the newly created database comprising of 5 different Presentation Attack Instruments (PAI) has demonstrated an outstanding performance on all 5 PAI through proposed approach. With the set ofcomplementary benefits of proposed approach illustrated in this work, we deduce the robustness towards unseen 2D attacks.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121087894","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 : 2017-10-01DOI: 10.1109/BTAS.2017.8272684
T. Neal, D. Woodard
Because passwords and personal identification numbers are easily forgotten, stolen, or reused on multiple accounts, the current norm for mobile device security is quickly becoming inefficient and inconvenient. Thus, manufacturers have worked to make physiological biometrics accessible to mobile device owners as improved security measures. While behavioral biometrics has yet to receive commercial attention, researchers have continued to consider these approaches as well. However, studies of interactive data are limited, and efforts which are aimed at improving the performance of such techniques remain relevant. Thus, this paper provides a performance analysis of application, Bluetooth, and Wi-Fi data collected from 189 subjects on a mobile device for user verification. Results indicate that user authentication can be achieved with up to 91% accuracy, demonstrating the effectiveness of associative classification as a feature extraction technique.
{"title":"Using associative classification to authenticate mobile device users","authors":"T. Neal, D. Woodard","doi":"10.1109/BTAS.2017.8272684","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272684","url":null,"abstract":"Because passwords and personal identification numbers are easily forgotten, stolen, or reused on multiple accounts, the current norm for mobile device security is quickly becoming inefficient and inconvenient. Thus, manufacturers have worked to make physiological biometrics accessible to mobile device owners as improved security measures. While behavioral biometrics has yet to receive commercial attention, researchers have continued to consider these approaches as well. However, studies of interactive data are limited, and efforts which are aimed at improving the performance of such techniques remain relevant. Thus, this paper provides a performance analysis of application, Bluetooth, and Wi-Fi data collected from 189 subjects on a mobile device for user verification. Results indicate that user authentication can be achieved with up to 91% accuracy, demonstrating the effectiveness of associative classification as a feature extraction technique.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"43 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124969286","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 : 2017-10-01DOI: 10.1109/BTAS.2017.8272738
Chris Murphy, Jiaju Huang, Daqing Hou, S. Schuckers
Conventional one-stop authentication of a computer terminal takes place at a user's initial sign-on. In contrast, continuous authentication protects against the case where an intruder takes over an authenticated terminal or simply has access to sign-on credentials. Behavioral biometrics has had some success in providing continuous authentication without requiring additional hardware. However, further advancement requires benchmarking existing algorithms against large, shared datasets. To this end, we provide a novel large dataset that captures not only keystrokes, but also mouse events and active programs. Our dataset is collected using passive logging software to monitor user interactions with the mouse, keyboard, and software programs. Data was collected from 103 users in a completely uncontrolled, natural setting, over a time span of 2.5 years. We apply Gunetti & Picardi's algorithm, a state-of-the-art algorithm in free text keystroke dynamics, as an initial benchmarkfor the new dataset.
{"title":"Shared dataset on natural human-computer interaction to support continuous authentication research","authors":"Chris Murphy, Jiaju Huang, Daqing Hou, S. Schuckers","doi":"10.1109/BTAS.2017.8272738","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272738","url":null,"abstract":"Conventional one-stop authentication of a computer terminal takes place at a user's initial sign-on. In contrast, continuous authentication protects against the case where an intruder takes over an authenticated terminal or simply has access to sign-on credentials. Behavioral biometrics has had some success in providing continuous authentication without requiring additional hardware. However, further advancement requires benchmarking existing algorithms against large, shared datasets. To this end, we provide a novel large dataset that captures not only keystrokes, but also mouse events and active programs. Our dataset is collected using passive logging software to monitor user interactions with the mouse, keyboard, and software programs. Data was collected from 103 users in a completely uncontrolled, natural setting, over a time span of 2.5 years. We apply Gunetti & Picardi's algorithm, a state-of-the-art algorithm in free text keystroke dynamics, as an initial benchmarkfor the new dataset.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127930750","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 : 2017-10-01DOI: 10.1109/BTAS.2017.8272751
R. H. Vareto, Samira Silva, F. Costa, W. R. Schwartz
Face Recognition is one of the most relevant problems in computer vision as we consider its importance to areas such as surveillance, forensics and psychology. Furthermore, open-set face recognition has a large room for improvement since only few researchers have focused on it. In fact, a real-world recognition system has to cope with several unseen individuals and determine whether a given face image is associated with a subject registered in a gallery of known individuals. In this work, we combine hashing functions and classification methods to estimate when probe samples are known (i.e., belong to the gallery set). We carry out experiments with partial least squares and neural networks and show how response value histograms tend to behave for known and unknown individuals whenever we test a probe sample. In addition, we conduct experiments on FRGCv1, PubFig83 and VGGFace to show that our method continues effective regardless of the dataset difficulty.
{"title":"Towards open-set face recognition using hashing functions","authors":"R. H. Vareto, Samira Silva, F. Costa, W. R. Schwartz","doi":"10.1109/BTAS.2017.8272751","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272751","url":null,"abstract":"Face Recognition is one of the most relevant problems in computer vision as we consider its importance to areas such as surveillance, forensics and psychology. Furthermore, open-set face recognition has a large room for improvement since only few researchers have focused on it. In fact, a real-world recognition system has to cope with several unseen individuals and determine whether a given face image is associated with a subject registered in a gallery of known individuals. In this work, we combine hashing functions and classification methods to estimate when probe samples are known (i.e., belong to the gallery set). We carry out experiments with partial least squares and neural networks and show how response value histograms tend to behave for known and unknown individuals whenever we test a probe sample. In addition, we conduct experiments on FRGCv1, PubFig83 and VGGFace to show that our method continues effective regardless of the dataset difficulty.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133316512","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 : 2017-10-01DOI: 10.1109/BTAS.2017.8272760
Yi Zhang, Houjun Huang, Haifeng Zhang, Liao Ni, W. Xu, N. U. Ahmed, Md. Shakil Ahmed, Yilun Jin, Ying Chen, Jingxuan Wen, Wenxin Li
In recent years, finger vein recognition has become an important sub-field in biometrics and been applied to real-world applications. The development of finger vein recognition algorithms heavily depends on large-scale real-world data sets. In order to motivate research on finger vein recognition, we released the largest finger vein data set up to now and hold finger vein recognition competitions based on our data set every year. In 2017, International Competition on Finger Vein Recognition (ICFVR) is held jointly with IJCB 2017. 11 teams registered and 10 of them joined the final evaluation. The winner of this year dramatically improved the EER from 2.64% to 0.483% compared to the 'winner of last year. In this paper, we introduce the process and results of ICFVR 2017 and give insights on development of state-of-art finger vein recognition algorithms.
{"title":"ICFVR 2017: 3rd international competition on finger vein recognition","authors":"Yi Zhang, Houjun Huang, Haifeng Zhang, Liao Ni, W. Xu, N. U. Ahmed, Md. Shakil Ahmed, Yilun Jin, Ying Chen, Jingxuan Wen, Wenxin Li","doi":"10.1109/BTAS.2017.8272760","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272760","url":null,"abstract":"In recent years, finger vein recognition has become an important sub-field in biometrics and been applied to real-world applications. The development of finger vein recognition algorithms heavily depends on large-scale real-world data sets. In order to motivate research on finger vein recognition, we released the largest finger vein data set up to now and hold finger vein recognition competitions based on our data set every year. In 2017, International Competition on Finger Vein Recognition (ICFVR) is held jointly with IJCB 2017. 11 teams registered and 10 of them joined the final evaluation. The winner of this year dramatically improved the EER from 2.64% to 0.483% compared to the 'winner of last year. In this paper, we introduce the process and results of ICFVR 2017 and give insights on development of state-of-art finger vein recognition algorithms.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124421728","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 : 2017-10-01DOI: 10.1109/BTAS.2017.8272748
Anurag Chowdhury, A. Ross
We present a deep learning based algorithm for speaker recognition from degraded audio signals. We use the commonly employed Mel-Frequency Cepstral Coefficients (MFCC) for representing the audio signals. A convolutional neural network (CNN) based on 1D filters, rather than 2D filters, is then designed. The filters in the CNN are designed to learn inter-dependency between cepstral coefficients extracted from audio frames of fixed temporal expanse. Our approach aims at extracting speaker dependent features, like Sub-glottal and Supra-glottal features, of the human speech production apparatus for identifying speakers from degraded audio signals. The performance of the proposed method is compared against existing baseline schemes on both synthetically and naturally corrupted speech data. Experiments convey the efficacy of the proposed architecture for speaker recognition.
{"title":"Extracting sub-glottal and Supra-glottal features from MFCC using convolutional neural networks for speaker identification in degraded audio signals","authors":"Anurag Chowdhury, A. Ross","doi":"10.1109/BTAS.2017.8272748","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272748","url":null,"abstract":"We present a deep learning based algorithm for speaker recognition from degraded audio signals. We use the commonly employed Mel-Frequency Cepstral Coefficients (MFCC) for representing the audio signals. A convolutional neural network (CNN) based on 1D filters, rather than 2D filters, is then designed. The filters in the CNN are designed to learn inter-dependency between cepstral coefficients extracted from audio frames of fixed temporal expanse. Our approach aims at extracting speaker dependent features, like Sub-glottal and Supra-glottal features, of the human speech production apparatus for identifying speakers from degraded audio signals. The performance of the proposed method is compared against existing baseline schemes on both synthetically and naturally corrupted speech data. Experiments convey the efficacy of the proposed architecture for speaker recognition.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132225129","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 : 2017-10-01DOI: 10.1109/BTAS.2017.8272747
Abdulaziz Alorf, A. L. Abbott
The current trend in image analysis is to employ automatically detected feature types, such as those obtained using deep-learning techniques. For some applications, however, manually crafted features such as Histogram of Oriented Gradients (HOG) continue to yield better performance in demanding situations. This paper considers both approaches for the problem of facial attribute classification, for images obtained “in the wild.” Attributes of particular interest are eye state (open/closed), mouth state (open/closed), and eyeglasses (present/absent). We present a full face-processing pipeline that employs conventional machine learning techniques, from detection to attribute classification. Experimental results have indicated better performance using RootSIFT with a conventional support-vector machine (SVM) approach, as compared to deep-learning approaches that have been reported in the literature. Our proposed open/closed eye classifier has yielded an accuracy of 99.3% on the CEW dataset, and an accuracy of 98.7% on the ZJU dataset. Similarly, our proposed open/closed mouth classifier has achieved performance similar to deep learning. Also, our proposed presence/absence eyeglasses classifier delivered very good performance, being the best method on LFWA, and second best for the CelebA dataset. The system reported here runs at 30 fps on HD-sized video using a CPU-only implementation.
{"title":"In defense of low-level structural features and SVMs for facial attribute classification: Application to detection of eye state, Mouth State, and eyeglasses in the wild","authors":"Abdulaziz Alorf, A. L. Abbott","doi":"10.1109/BTAS.2017.8272747","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272747","url":null,"abstract":"The current trend in image analysis is to employ automatically detected feature types, such as those obtained using deep-learning techniques. For some applications, however, manually crafted features such as Histogram of Oriented Gradients (HOG) continue to yield better performance in demanding situations. This paper considers both approaches for the problem of facial attribute classification, for images obtained “in the wild.” Attributes of particular interest are eye state (open/closed), mouth state (open/closed), and eyeglasses (present/absent). We present a full face-processing pipeline that employs conventional machine learning techniques, from detection to attribute classification. Experimental results have indicated better performance using RootSIFT with a conventional support-vector machine (SVM) approach, as compared to deep-learning approaches that have been reported in the literature. Our proposed open/closed eye classifier has yielded an accuracy of 99.3% on the CEW dataset, and an accuracy of 98.7% on the ZJU dataset. Similarly, our proposed open/closed mouth classifier has achieved performance similar to deep learning. Also, our proposed presence/absence eyeglasses classifier delivered very good performance, being the best method on LFWA, and second best for the CelebA dataset. The system reported here runs at 30 fps on HD-sized video using a CPU-only implementation.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132609026","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 : 2017-10-01DOI: 10.1109/BTAS.2017.8272718
P. Drozdowski, C. Rathgeb, H. Hofbauer, J. Wagner, A. Uhl, C. Busch
The necessity of biometric template alignment imposes a significant computational load and increases the probability of false positive occurrences in biometric systems. While for some modalities, automatic pre-alignment of biometric samples is utilised, this topic has not yet been explored for systems based on the iris. This paper presents a method for pre-alignment of iris images based on the positions ofautomatically detected eye corners. Existing work in the area of automatic eye corner detection has hitherto only involved visible wavelength images; for the near-infrared images, used in the vast majority of current iris recognition systems, this task is significantly more challenging and as of yet unexplored. A comparative study of two methods for solving this problem is presented in this paper. The eye corners detected by the two methods are then used for the pre-alignment and biometric performance evaluation experiments. The system utilising image pre-alignment is benchmarked against a baseline iris recognition system on the iris subset of the BioSecure database. In the benchmark, the workload associated with alignment compensation is significantly reduced, while the biometric performance remains unchanged or even improves slightly.
{"title":"Towards pre-alignment of near-infrared iris images","authors":"P. Drozdowski, C. Rathgeb, H. Hofbauer, J. Wagner, A. Uhl, C. Busch","doi":"10.1109/BTAS.2017.8272718","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272718","url":null,"abstract":"The necessity of biometric template alignment imposes a significant computational load and increases the probability of false positive occurrences in biometric systems. While for some modalities, automatic pre-alignment of biometric samples is utilised, this topic has not yet been explored for systems based on the iris. This paper presents a method for pre-alignment of iris images based on the positions ofautomatically detected eye corners. Existing work in the area of automatic eye corner detection has hitherto only involved visible wavelength images; for the near-infrared images, used in the vast majority of current iris recognition systems, this task is significantly more challenging and as of yet unexplored. A comparative study of two methods for solving this problem is presented in this paper. The eye corners detected by the two methods are then used for the pre-alignment and biometric performance evaluation experiments. The system utilising image pre-alignment is benchmarked against a baseline iris recognition system on the iris subset of the BioSecure database. In the benchmark, the workload associated with alignment compensation is significantly reduced, while the biometric performance remains unchanged or even improves slightly.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114911039","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 : 2017-10-01DOI: 10.1109/BTAS.2017.8272720
Kun Su, Gongping Yang, Lu Yang, Yilong Yin
Efficient identification of finger veins is still a challenging problem due to the increasing size of the finger vein database. Most leading finger vein image identification methods have high-dimensional real-valued features, which result in extremely high computation complexity. Hashing algorithms are extraordinary effective ways to facilitate finger vein image retrieval. Therefore, in this paper, we proposed a finger vein image retrieval scheme based on Affinity-Preserving K-means Hashing (APKMH) algorithm and bag of subspaces based image feature. At first, we represent finger vein image by Nonlinearly Sub-space Coding (NSC) method which can obtain the discriminative finger vein image features. Then the features space is partitioned into multiple subsegments. In each subsegment, we employ the APKMH algorithm, which can simultaneously construct the visual codebook by directly k-means clustering and encode the feature vector as the binary index of the codeword. Experimental results on a large fused finger vein dataset demonstrate that our hashing method outperforms the state-of-the-art finger vein retrieval methods.
{"title":"Finger vein image retrieval via affinity-preserving K-means hashing","authors":"Kun Su, Gongping Yang, Lu Yang, Yilong Yin","doi":"10.1109/BTAS.2017.8272720","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272720","url":null,"abstract":"Efficient identification of finger veins is still a challenging problem due to the increasing size of the finger vein database. Most leading finger vein image identification methods have high-dimensional real-valued features, which result in extremely high computation complexity. Hashing algorithms are extraordinary effective ways to facilitate finger vein image retrieval. Therefore, in this paper, we proposed a finger vein image retrieval scheme based on Affinity-Preserving K-means Hashing (APKMH) algorithm and bag of subspaces based image feature. At first, we represent finger vein image by Nonlinearly Sub-space Coding (NSC) method which can obtain the discriminative finger vein image features. Then the features space is partitioned into multiple subsegments. In each subsegment, we employ the APKMH algorithm, which can simultaneously construct the visual codebook by directly k-means clustering and encode the feature vector as the binary index of the codeword. Experimental results on a large fused finger vein dataset demonstrate that our hashing method outperforms the state-of-the-art finger vein retrieval methods.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123957306","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 : 2017-10-01DOI: 10.1109/BTAS.2017.8272703
Huibin Li, Jian Sun, Liming Chen
This paper presents a straight-forward yet efficient, and expression-robust 3D face recognition approach by exploring location sensitive sparse representation of deep normal patterns (DNP). In particular, given raw 3D facial surfaces, we first run 3D face pre-processing pipeline, including nose tip detection, face region cropping, and pose normalization. The 3D coordinates of each normalized 3D facial surface are then projected into 2D plane to generate geometry images, from which three images of facial surface normal components are estimated. Each normal image is then fed into a pre-trained deep face net to generate deep representations of facial surface normals, i.e., deep normal patterns. Considering the importance of different facial locations, we propose a location sensitive sparse representation classifier (LS-SRC) for similarity measure among deep normal patterns associated with different 3D faces. Finally, simple score-level fusion of different normal components are used for the final decision. The proposed approach achieves significantly high performance, and reporting rank-one scores of 98.01%, 97.60%, and 96.13% on the FRGC v2.0, Bosphorus, and BU-3DFE databases when only one sample per subject is used in the gallery. These experimental results reveals that the performance of 3D face recognition would be constantly improved with the aid of training deep models from massive 2D face images, which opens the door for future directions of 3D face recognition.
本文通过探索深度正常模式(deep normal patterns, DNP)的位置敏感稀疏表示，提出了一种简单、高效、表达鲁棒的3D人脸识别方法。特别是，给定原始的3D面部表面，我们首先运行3D面部预处理管道，包括鼻尖检测，面部区域裁剪和姿态归一化。然后将每个归一化的三维人脸表面的三维坐标投影到二维平面上生成几何图像，从中估计出人脸表面法线分量的三幅图像。然后将每个法线图像馈送到预训练的深度人脸网络中，以生成面部表面法线的深度表示，即深度法线模式。考虑到不同人脸位置的重要性，我们提出了一种位置敏感的稀疏表示分类器(LS-SRC)，用于测量与不同3D人脸相关的深度法向模式之间的相似性。最后，使用不同正常分量的简单分数级融合进行最终判定。该方法取得了显著的高性能，当图库中每个受试者仅使用一个样本时，在FRGC v2.0、Bosphorus和BU-3DFE数据库上的排名得分分别为98.01%、97.60%和96.13%。这些实验结果表明，通过对大量二维人脸图像进行深度模型训练，可以不断提高三维人脸识别的性能，为未来的三维人脸识别方向打开了大门。
{"title":"Location-sensitive sparse representation of deep normal patterns for expression-robust 3D face recognition","authors":"Huibin Li, Jian Sun, Liming Chen","doi":"10.1109/BTAS.2017.8272703","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272703","url":null,"abstract":"This paper presents a straight-forward yet efficient, and expression-robust 3D face recognition approach by exploring location sensitive sparse representation of deep normal patterns (DNP). In particular, given raw 3D facial surfaces, we first run 3D face pre-processing pipeline, including nose tip detection, face region cropping, and pose normalization. The 3D coordinates of each normalized 3D facial surface are then projected into 2D plane to generate geometry images, from which three images of facial surface normal components are estimated. Each normal image is then fed into a pre-trained deep face net to generate deep representations of facial surface normals, i.e., deep normal patterns. Considering the importance of different facial locations, we propose a location sensitive sparse representation classifier (LS-SRC) for similarity measure among deep normal patterns associated with different 3D faces. Finally, simple score-level fusion of different normal components are used for the final decision. The proposed approach achieves significantly high performance, and reporting rank-one scores of 98.01%, 97.60%, and 96.13% on the FRGC v2.0, Bosphorus, and BU-3DFE databases when only one sample per subject is used in the gallery. These experimental results reveals that the performance of 3D face recognition would be constantly improved with the aid of training deep models from massive 2D face images, which opens the door for future directions of 3D face recognition.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128974683","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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