Pub Date : 2017-10-01DOI: 10.1109/BTAS.2017.8272749
Sudipta Banerjee, A. Ross
Iris recognition entails the use of iris images to recognize an individual. In some cases, the iris image acquired from an individual can be modified by subjecting it to successive photometric transformations such as brightening, gamma correction, median filtering and Gaussian smoothing, resulting in a family of transformed images. Automatically inferring the relationship between the set of transformed images is important in the context of digital image forensics. In this regard, we develop a method to generate an Image Phylogeny Tree (IPT) from a set of such transformed images. Our strategy entails modeling an arbitrary photometric transformation as a linear or non-linear function and utilizing the parameters of the model to quantify the relationship between pairs of images. The estimated parameters are then used to generate the IPT. Modest, yet promising, results are obtained in terms of parameter estimation and IPT generation.
{"title":"Computing an image Phylogeny Tree from photometrically modified iris images","authors":"Sudipta Banerjee, A. Ross","doi":"10.1109/BTAS.2017.8272749","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272749","url":null,"abstract":"Iris recognition entails the use of iris images to recognize an individual. In some cases, the iris image acquired from an individual can be modified by subjecting it to successive photometric transformations such as brightening, gamma correction, median filtering and Gaussian smoothing, resulting in a family of transformed images. Automatically inferring the relationship between the set of transformed images is important in the context of digital image forensics. In this regard, we develop a method to generate an Image Phylogeny Tree (IPT) from a set of such transformed images. Our strategy entails modeling an arbitrary photometric transformation as a linear or non-linear function and utilizing the parameters of the model to quantify the relationship between pairs of images. The estimated parameters are then used to generate the IPT. Modest, yet promising, results are obtained in terms of parameter estimation and IPT generation.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"1 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":"130798426","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.2017.8272746
Nisha Srinivas, Ryan Tokola, A. Mikkilineni, I. Nookaew, M. Leuze, Chris Boehnen
In this paper we introduce the concept of correlating genetic variations in an individual's specific genetic code (DNA) and facial morphology. This is the first step in the research effort to estimate facial appearance from DNA samples, which is gaining momentum within intelligence, law enforcement and national security communities. The dataset for the study consisting of genetic data and 3D facial scans (phenotype) data was obtained through the FaceBase Consortium. The proposed approach has three main steps: phenotype feature extraction from 3D face images, genotype feature extraction from a DNA sample, and genome-wide association analysis to determine genetic variations that contribute to facial structure and appearance. Results indicate that there exist significant correlations between genetic information and facial structure. We have identified 30 single nucleotide polymorphisms (SNPs), i.e. genetic variations, that significantly contribute to facial structure and appearance. We conclude with a preliminary attempt at facial reconstruction from the genetic data and emphasize on the complexity of the problem and the challenges encountered.
{"title":"DNA2FACE: An approach to correlating 3D facial structure and DNA","authors":"Nisha Srinivas, Ryan Tokola, A. Mikkilineni, I. Nookaew, M. Leuze, Chris Boehnen","doi":"10.1109/BTAS.2017.8272746","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272746","url":null,"abstract":"In this paper we introduce the concept of correlating genetic variations in an individual's specific genetic code (DNA) and facial morphology. This is the first step in the research effort to estimate facial appearance from DNA samples, which is gaining momentum within intelligence, law enforcement and national security communities. The dataset for the study consisting of genetic data and 3D facial scans (phenotype) data was obtained through the FaceBase Consortium. The proposed approach has three main steps: phenotype feature extraction from 3D face images, genotype feature extraction from a DNA sample, and genome-wide association analysis to determine genetic variations that contribute to facial structure and appearance. Results indicate that there exist significant correlations between genetic information and facial structure. We have identified 30 single nucleotide polymorphisms (SNPs), i.e. genetic variations, that significantly contribute to facial structure and appearance. We conclude with a preliminary attempt at facial reconstruction from the genetic data and emphasize on the complexity of the problem and the challenges encountered.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"75 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":"122832353","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.2017.8272733
Pengfei Dou, I. Kakadiaris
Image-based 3D face reconstruction has great potential in different areas, such as facial recognition, facial analysis, and facial animation. Due to the variations in image quality, single-image-based 3D face reconstruction might not be sufficient to accurately reconstruct a 3D face. To overcome this limitation, multi-view 3D face reconstruction uses multiple images of the same subject and aggregates complementary information for better accuracy. Though theoretically appealing, there are multiple challenges in practice. Among these challenges, the most significant is that it is difficult to establish coherent and accurate correspondence among a set of images, especially when these images are captured in different conditions. In this paper, we propose a method, Deep Recurrent 3D FAce Reconstruction (DRFAR), to solve the task ofmulti-view 3D face reconstruction using a subspace representation of the 3D facial shape and a deep recurrent neural network that consists of both a deep con-volutional neural network (DCNN) and a recurrent neural network (RNN). The DCNN disentangles the facial identity and the facial expression components for each single image independently, while the RNN fuses identity-related features from the DCNN and aggregates the identity specific contextual information, or the identity signal, from the whole set of images to predict the facial identity parameter, which is robust to variations in image quality and is consistent over the whole set of images. Through extensive experiments, we evaluate our proposed method and demonstrate its superiority over existing methods.
基于图像的三维人脸重建在人脸识别、人脸分析和人脸动画等不同领域具有巨大的潜力。由于图像质量的差异，基于单图像的3D人脸重建可能不足以准确地重建3D人脸。为了克服这一限制，多视图3D人脸重建使用同一主题的多幅图像并聚合互补信息以提高准确性。虽然理论上很有吸引力，但在实践中存在多重挑战。在这些挑战中，最重要的是很难在一组图像之间建立连贯和准确的对应关系，特别是当这些图像在不同条件下捕获时。本文提出了一种深度递归3D人脸重建(Deep Recurrent 3D FAce Reconstruction, DRFAR)方法，该方法利用三维人脸形状的子空间表示和由深度卷积神经网络(DCNN)和递归神经网络(RNN)组成的深度递归神经网络来解决多视图3D人脸重建任务。DCNN可以独立分离每张图像的面部身份和面部表情成分，而RNN则融合来自DCNN的身份相关特征，并聚合来自整组图像的身份特定上下文信息或身份信号来预测面部身份参数，该参数对图像质量的变化具有鲁棒性，并且在整组图像上保持一致。通过大量的实验，我们评估了我们提出的方法，并证明了它比现有方法的优越性。
{"title":"Multi-view 3D face reconstruction with deep recurrent neural networks","authors":"Pengfei Dou, I. Kakadiaris","doi":"10.1109/BTAS.2017.8272733","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272733","url":null,"abstract":"Image-based 3D face reconstruction has great potential in different areas, such as facial recognition, facial analysis, and facial animation. Due to the variations in image quality, single-image-based 3D face reconstruction might not be sufficient to accurately reconstruct a 3D face. To overcome this limitation, multi-view 3D face reconstruction uses multiple images of the same subject and aggregates complementary information for better accuracy. Though theoretically appealing, there are multiple challenges in practice. Among these challenges, the most significant is that it is difficult to establish coherent and accurate correspondence among a set of images, especially when these images are captured in different conditions. In this paper, we propose a method, Deep Recurrent 3D FAce Reconstruction (DRFAR), to solve the task ofmulti-view 3D face reconstruction using a subspace representation of the 3D facial shape and a deep recurrent neural network that consists of both a deep con-volutional neural network (DCNN) and a recurrent neural network (RNN). The DCNN disentangles the facial identity and the facial expression components for each single image independently, while the RNN fuses identity-related features from the DCNN and aggregates the identity specific contextual information, or the identity signal, from the whole set of images to predict the facial identity parameter, which is robust to variations in image quality and is consistent over the whole set of images. Through extensive experiments, we evaluate our proposed method and demonstrate its superiority over existing methods.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"28 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":"126606987","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.2017.8272711
Javier Galbally, M. Gomez-Barrero, A. Ross
Traditionally, the accuracy of signature verification systems has been evaluated following a protocol that considers two independent impostor scenarios: random forgeries and skilled forgeries. Although such an approach is not necessarily incorrect, it can lead to a misinterpretation of the results of the assessment process. Furthermore, such a full separation between both types of impostors may be unrealistic in many operational real-world applications. The current article discusses the soundness of the random-skilled impostor dichotomy and proposes complementary approaches to report the accuracy of signature verification systems, discussing their advantages and limitations.
{"title":"Accuracy evaluation of handwritten signature verification: Rethinking the random-skilled forgeries dichotomy","authors":"Javier Galbally, M. Gomez-Barrero, A. Ross","doi":"10.1109/BTAS.2017.8272711","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272711","url":null,"abstract":"Traditionally, the accuracy of signature verification systems has been evaluated following a protocol that considers two independent impostor scenarios: random forgeries and skilled forgeries. Although such an approach is not necessarily incorrect, it can lead to a misinterpretation of the results of the assessment process. Furthermore, such a full separation between both types of impostors may be unrealistic in many operational real-world applications. The current article discusses the soundness of the random-skilled impostor dichotomy and proposes complementary approaches to report the accuracy of signature verification systems, discussing their advantages and limitations.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"316 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113958766","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.2017.8272765
Rui Shao, X. Lan, P. Yuen
3D mask spoofing attack has been one of the main challenges in face recognition. A real face displays a different motion behaviour compared to a 3D mask spoof attempt, which is reflected by different facial dynamic textures. However, the different dynamic information usually exists in the subtle texture level, which cannot be fully differentiated by traditional hand-crafted texture-based methods. In this paper, we propose a novel method for 3D mask face anti-spoofing, namely deep convolutional dynamic texture learning, which learns robust dynamic texture information from fine-grained deep convolutional features. Moreover, channel-discriminability constraint is adaptively incorporated to weight the discriminability of feature channels in the learning process. Experiments on both public datasets validate that the proposed method achieves promising results under intra and cross dataset scenario.
{"title":"Deep convolutional dynamic texture learning with adaptive channel-discriminability for 3D mask face anti-spoofing","authors":"Rui Shao, X. Lan, P. Yuen","doi":"10.1109/BTAS.2017.8272765","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272765","url":null,"abstract":"3D mask spoofing attack has been one of the main challenges in face recognition. A real face displays a different motion behaviour compared to a 3D mask spoof attempt, which is reflected by different facial dynamic textures. However, the different dynamic information usually exists in the subtle texture level, which cannot be fully differentiated by traditional hand-crafted texture-based methods. In this paper, we propose a novel method for 3D mask face anti-spoofing, namely deep convolutional dynamic texture learning, which learns robust dynamic texture information from fine-grained deep convolutional features. Moreover, channel-discriminability constraint is adaptively incorporated to weight the discriminability of feature channels in the learning process. Experiments on both public datasets validate that the proposed method achieves promising results under intra and cross dataset scenario.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"82 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":"126283746","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.2017.8272719
Guangcan Mai, M. Lim, P. Yuen
A security index for biometric systems is essential because biometrics have been widely adopted as a secure authentication component in critical systems. Most of bio-metric systems secured by template protection schemes are based on binary templates. To adopt popular template protection schemes such as fuzzy commitment and fuzzy extractor that can be applied on binary templates only, non-binary templates (e.g., real-valued, point-set based) need to be converted to binary. However, existing security measurements for binary template based biometric systems either cannot reflect the actual attack difficulties or are too computationally expensive to be practical. This paper presents an acceleration of the guessing entropy which reflects the expected number of guessing trials in attacking the binary template based biometric systems. The acceleration benefits from computation reuse and pruning. Experimental results on two datasets show that the acceleration has more than 6x, 20x, and 200x speed up without losing the estimation accuracy in different system settings.
{"title":"On the guessability of binary biometric templates: A practical guessing entropy based approach","authors":"Guangcan Mai, M. Lim, P. Yuen","doi":"10.1109/BTAS.2017.8272719","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272719","url":null,"abstract":"A security index for biometric systems is essential because biometrics have been widely adopted as a secure authentication component in critical systems. Most of bio-metric systems secured by template protection schemes are based on binary templates. To adopt popular template protection schemes such as fuzzy commitment and fuzzy extractor that can be applied on binary templates only, non-binary templates (e.g., real-valued, point-set based) need to be converted to binary. However, existing security measurements for binary template based biometric systems either cannot reflect the actual attack difficulties or are too computationally expensive to be practical. This paper presents an acceleration of the guessing entropy which reflects the expected number of guessing trials in attacking the binary template based biometric systems. The acceleration benefits from computation reuse and pruning. Experimental results on two datasets show that the acceleration has more than 6x, 20x, and 200x speed up without losing the estimation accuracy in different system settings.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"88 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":"126350922","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.2017.8272768
Russel Mesbah, B. McCane, S. Mills
Sclera segmentation as an ocular biometric has been of an interest in a variety of security and medical applications. The current approaches mostly rely on handcrafted features which make the generalisation of the learnt hypothesis challenging encountering images taken from various angles, and in different visible light spectrums. Convolutional Neural Networks (CNNs) are capable of extracting the corresponding features automatically. Despite the fact that CNNs showed a remarkable performance in a variety of image semantic segmentations, the output can be noisy and less accurate particularly in object boundaries. To address this issue, we have used Conditional Random Fields (CRFs) to regulate the CNN outputs. The results of applying this technique to sclera segmentation dataset (SSERBC 2017) are comparable with the state of the art solutions.
{"title":"Conditional random fields incorporate convolutional neural networks for human eye sclera semantic segmentation","authors":"Russel Mesbah, B. McCane, S. Mills","doi":"10.1109/BTAS.2017.8272768","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272768","url":null,"abstract":"Sclera segmentation as an ocular biometric has been of an interest in a variety of security and medical applications. The current approaches mostly rely on handcrafted features which make the generalisation of the learnt hypothesis challenging encountering images taken from various angles, and in different visible light spectrums. Convolutional Neural Networks (CNNs) are capable of extracting the corresponding features automatically. Despite the fact that CNNs showed a remarkable performance in a variety of image semantic segmentations, the output can be noisy and less accurate particularly in object boundaries. To address this issue, we have used Conditional Random Fields (CRFs) to regulate the CNN outputs. The results of applying this technique to sclera segmentation dataset (SSERBC 2017) are comparable with the state of the art solutions.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"480 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":"123054720","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.2017.8272721
G. Hsu, Arulmurugan Ambikapathi, Ming Chen
Accurate pulse estimation is of pivotal importance in acquiring the critical physical conditions of human subjects under test, and facial video based pulse estimation approaches recently gained attention owing to their simplicity. In this work, we have endeavored to develop a novel deep learning approach as the core part for pulse (heart rate) estimation by using a common RGB camera. Our approach consists of four steps. We first begin by detecting the face and its landmarks, and thereby locate the required facial ROI. In Step 2, we extract the sample mean sequences of the R, G, and B channels from the facial ROI, and explore three processing schemes for noise removal and signal enhancement. In Step 3, the Short-Time Fourier Transform (STFT) is employed to build the 2D Time-Frequency Representations (TFRs) of the sequences. The 2D TFR enables the formulation of the pulse estimation as an image-based classification problem, which can be solved in Step 4 by a deep Con-volutional Neural Network (CNN). Our approach is one of the pioneering works for attempting real-time pulse estimation using a deep learning framework. We have developed a pulse database, called the Pulse from Face (PFF), and used it to train the CNN. The PFF database will be made publicly available to advance related research. When compared to state-of-the-art pulse estimation approaches on the standard MAHNOB-HCI database, the proposed approach has exhibited superior performance.
{"title":"Deep learning with time-frequency representation for pulse estimation from facial videos","authors":"G. Hsu, Arulmurugan Ambikapathi, Ming Chen","doi":"10.1109/BTAS.2017.8272721","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272721","url":null,"abstract":"Accurate pulse estimation is of pivotal importance in acquiring the critical physical conditions of human subjects under test, and facial video based pulse estimation approaches recently gained attention owing to their simplicity. In this work, we have endeavored to develop a novel deep learning approach as the core part for pulse (heart rate) estimation by using a common RGB camera. Our approach consists of four steps. We first begin by detecting the face and its landmarks, and thereby locate the required facial ROI. In Step 2, we extract the sample mean sequences of the R, G, and B channels from the facial ROI, and explore three processing schemes for noise removal and signal enhancement. In Step 3, the Short-Time Fourier Transform (STFT) is employed to build the 2D Time-Frequency Representations (TFRs) of the sequences. The 2D TFR enables the formulation of the pulse estimation as an image-based classification problem, which can be solved in Step 4 by a deep Con-volutional Neural Network (CNN). Our approach is one of the pioneering works for attempting real-time pulse estimation using a deep learning framework. We have developed a pulse database, called the Pulse from Face (PFF), and used it to train the CNN. The PFF database will be made publicly available to advance related research. When compared to state-of-the-art pulse estimation approaches on the standard MAHNOB-HCI database, the proposed approach has exhibited superior performance.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"604 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":"129215996","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.2017.8272713
Yousef Atoum, Yaojie Liu, Amin Jourabloo, Xiaoming Liu
The face image is the most accessible biometric modality which is used for highly accurate face recognition systems, while it is vulnerable to many different types of presentation attacks. Face anti-spoofing is a very critical step before feeding the face image to biometric systems. In this paper, we propose a novel two-stream CNN-based approach for face anti-spoofing, by extracting the local features and holistic depth maps from the face images. The local features facilitate CNN to discriminate the spoof patches independent of the spatial face areas. On the other hand, holistic depth map examine whether the input image has a face-like depth. Extensive experiments are conducted on the challenging databases (CASIA-FASD, MSU-USSA, and Replay Attack), with comparison to the state of the art.
{"title":"Face anti-spoofing using patch and depth-based CNNs","authors":"Yousef Atoum, Yaojie Liu, Amin Jourabloo, Xiaoming Liu","doi":"10.1109/BTAS.2017.8272713","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272713","url":null,"abstract":"The face image is the most accessible biometric modality which is used for highly accurate face recognition systems, while it is vulnerable to many different types of presentation attacks. Face anti-spoofing is a very critical step before feeding the face image to biometric systems. In this paper, we propose a novel two-stream CNN-based approach for face anti-spoofing, by extracting the local features and holistic depth maps from the face images. The local features facilitate CNN to discriminate the spoof patches independent of the spatial face areas. On the other hand, holistic depth map examine whether the input image has a face-like depth. Extensive experiments are conducted on the challenging databases (CASIA-FASD, MSU-USSA, and Replay Attack), with comparison to the state of the art.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"41 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":"121188977","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.2017.8272769
Ning Jia, Victor Sanchez, Chang-Tsun Li
Gait recognition can be performed without subject cooperation under harsh conditions, thus it is an important tool in forensic gait analysis, security control, and other commercial applications. One critical issue that prevents gait recognition systems from being widely accepted is the performance drop when the camera viewpoint varies between the registered templates and the query data. In this paper, we explore the potential of combining feature optimisers and representations learned by convolutional neural networks (CNN) to achieve efficient view-invariant gait recognition. The experimental results indicate that CNN learns highly discriminative representations across moderate view variations, and these representations can be further improved using view-invariant feature selectors, achieving a high matching accuracy across views.
{"title":"Learning optimised representations for view-invariant gait recognition","authors":"Ning Jia, Victor Sanchez, Chang-Tsun Li","doi":"10.1109/BTAS.2017.8272769","DOIUrl":"https://doi.org/10.1109/BTAS.2017.8272769","url":null,"abstract":"Gait recognition can be performed without subject cooperation under harsh conditions, thus it is an important tool in forensic gait analysis, security control, and other commercial applications. One critical issue that prevents gait recognition systems from being widely accepted is the performance drop when the camera viewpoint varies between the registered templates and the query data. In this paper, we explore the potential of combining feature optimisers and representations learned by convolutional neural networks (CNN) to achieve efficient view-invariant gait recognition. The experimental results indicate that CNN learns highly discriminative representations across moderate view variations, and these representations can be further improved using view-invariant feature selectors, achieving a high matching accuracy across views.","PeriodicalId":372008,"journal":{"name":"2017 IEEE International Joint Conference on Biometrics (IJCB)","volume":"29 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":"116031842","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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