Pub Date : 2019-01-01DOI: 10.1109/ISBA.2019.8778566
C. Vorugunti, D. S. Guru, Viswanath Pulabaigari
Online Signature Verification (OSV) is a pattern recognition problem, which involves analysis of discrete-time signals of signature samples to classify them as genuine or forgery. One of the core difficulties in designing online signature verification (OSV) system is the inherent intra-writer variability in genuine handwritten signatures, combined with the likelihood of close resemblances and dissimilarities of skilled forgeries with the genuine signatures. To address this issue, in this manuscript, we emphasize the concept of writer dependent parameter fixation (i.e. features, decision threshold and feature dimension) using interval valued representation grounded on feature fusion. For an individual writer, a subset of discriminative features is selected from the original set of features using feature clustering techniques. This is at variance with the writer independent models in which common features are used for all the writers. To practically exhibit the efficiency of the proposed model, thorough experiments are carried out on benchmarking online signature datasets MCYT-100 (DB1), MCYT-330 (DB2) consist of signatures of 100, 330 individuals respectively. Experimental result confirms the efficiency of writer specific parameters for online signature verification. The EER value, the model computes, is lower compared to various latest signature verification models.
{"title":"An Efficient Online Signature Verification Based on Feature Fusion and Interval Valued Representation of Writer Specific Features","authors":"C. Vorugunti, D. S. Guru, Viswanath Pulabaigari","doi":"10.1109/ISBA.2019.8778566","DOIUrl":"https://doi.org/10.1109/ISBA.2019.8778566","url":null,"abstract":"Online Signature Verification (OSV) is a pattern recognition problem, which involves analysis of discrete-time signals of signature samples to classify them as genuine or forgery. One of the core difficulties in designing online signature verification (OSV) system is the inherent intra-writer variability in genuine handwritten signatures, combined with the likelihood of close resemblances and dissimilarities of skilled forgeries with the genuine signatures. To address this issue, in this manuscript, we emphasize the concept of writer dependent parameter fixation (i.e. features, decision threshold and feature dimension) using interval valued representation grounded on feature fusion. For an individual writer, a subset of discriminative features is selected from the original set of features using feature clustering techniques. This is at variance with the writer independent models in which common features are used for all the writers. To practically exhibit the efficiency of the proposed model, thorough experiments are carried out on benchmarking online signature datasets MCYT-100 (DB1), MCYT-330 (DB2) consist of signatures of 100, 330 individuals respectively. Experimental result confirms the efficiency of writer specific parameters for online signature verification. The EER value, the model computes, is lower compared to various latest signature verification models.","PeriodicalId":270033,"journal":{"name":"2019 IEEE 5th International Conference on Identity, Security, and Behavior Analysis (ISBA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125928020","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 : 2019-01-01DOI: 10.1109/ISBA.2019.8778466
Malvika Singh, B. Mehtre, S. Sangeetha
The greatest threat towards securing the organization and its assets are no longer the attackers attacking beyond the network walls of the organization but the insiders present within the organization with malicious intent. Existing approaches helps to monitor, detect and prevent any malicious activities within an organization’s network while ignoring the human behavior impact on security. In this paper we have focused on user behavior profiling approach to monitor and analyze user behavior action sequence to detect insider threats. We present an ensemble hybrid machine learning approach using Multi State Long Short Term Memory (MSLSTM) and Convolution Neural Networks (CNN) based time series anomaly detection to detect the additive outliers in the behavior patterns based on their spatial-temporal behavior features. We find that using Multistate LSTM is better than basic single state LSTM. The proposed method with Multistate LSTM can successfully detect the insider threats providing the AUC of 0.9042 on train data and AUC of 0.9047 on test data when trained with publically available dataset for insider threats.
{"title":"User Behavior Profiling using Ensemble Approach for Insider Threat Detection","authors":"Malvika Singh, B. Mehtre, S. Sangeetha","doi":"10.1109/ISBA.2019.8778466","DOIUrl":"https://doi.org/10.1109/ISBA.2019.8778466","url":null,"abstract":"The greatest threat towards securing the organization and its assets are no longer the attackers attacking beyond the network walls of the organization but the insiders present within the organization with malicious intent. Existing approaches helps to monitor, detect and prevent any malicious activities within an organization’s network while ignoring the human behavior impact on security. In this paper we have focused on user behavior profiling approach to monitor and analyze user behavior action sequence to detect insider threats. We present an ensemble hybrid machine learning approach using Multi State Long Short Term Memory (MSLSTM) and Convolution Neural Networks (CNN) based time series anomaly detection to detect the additive outliers in the behavior patterns based on their spatial-temporal behavior features. We find that using Multistate LSTM is better than basic single state LSTM. The proposed method with Multistate LSTM can successfully detect the insider threats providing the AUC of 0.9042 on train data and AUC of 0.9047 on test data when trained with publically available dataset for insider threats.","PeriodicalId":270033,"journal":{"name":"2019 IEEE 5th International Conference on Identity, Security, and Behavior Analysis (ISBA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115306711","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 : 2019-01-01DOI: 10.1109/ISBA.2019.8778494
Anuja Dixit, Soumen Bag
Copy-move forgery is one of the most regarded image forgery technique to tamper information conveyed by the image. In this technique, segment of original image is replicated and pasted across the same image to produce forged image. This technique is capable to hide selective information or to add fictitious details in image. Detection of this form of forgery is one of the significant area of information security. In this paper, we propose block-based approach for copy-move image forgery detection to secure information conveyed through the image by identifying the forged images and to prevent spreading of tampered subject matter. Proposed model divides suspicious image in overlapping blocks. We extracted block features using Histogram of Oriented Gradients (HOG) and Singular Value Decomposition (SVD). Lexicographical sorting is performed over feature matrix followed by Euclidean distance computation to recognize similar feature vectors. To remove false match detection, Connected component labeling is utilized. Our scheme achieves highest F-measure than former techniques, when forged image sustain plain multiple copy-move, multiple copy-move with contrast adjustment, color reduction, and image blurring attacks.
{"title":"Utilization of HOG-SVD based Features with Connected Component Labeling for Multiple Copy-move Image Forgery Detection","authors":"Anuja Dixit, Soumen Bag","doi":"10.1109/ISBA.2019.8778494","DOIUrl":"https://doi.org/10.1109/ISBA.2019.8778494","url":null,"abstract":"Copy-move forgery is one of the most regarded image forgery technique to tamper information conveyed by the image. In this technique, segment of original image is replicated and pasted across the same image to produce forged image. This technique is capable to hide selective information or to add fictitious details in image. Detection of this form of forgery is one of the significant area of information security. In this paper, we propose block-based approach for copy-move image forgery detection to secure information conveyed through the image by identifying the forged images and to prevent spreading of tampered subject matter. Proposed model divides suspicious image in overlapping blocks. We extracted block features using Histogram of Oriented Gradients (HOG) and Singular Value Decomposition (SVD). Lexicographical sorting is performed over feature matrix followed by Euclidean distance computation to recognize similar feature vectors. To remove false match detection, Connected component labeling is utilized. Our scheme achieves highest F-measure than former techniques, when forged image sustain plain multiple copy-move, multiple copy-move with contrast adjustment, color reduction, and image blurring attacks.","PeriodicalId":270033,"journal":{"name":"2019 IEEE 5th International Conference on Identity, Security, and Behavior Analysis (ISBA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129126290","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 : 2019-01-01DOI: 10.1109/ISBA.2019.8778529
M. Vivekanandan, V. N. Sastry, U. S. Reddy
Mobile user authentication is a challenging task in the mobile cloud computing (MCC). In 2015, Tsai and Lo’s developed authentication protocol in distributed MCC. Which is vulnerable to the biometric misuse, incorrect login credentials (password and fingerprint) and attacks for service provider impersonation. It has no provision for smart-card revocation and lacks mutual authentication. To address this-mentioned issues, we propose a novel Biometric based User Authentication Protocol for MCC. The proposed protocol supports session key agreement of participants and flawless mutual authentication. Our protocol is verified using Burrows-Abadi-Needham (BAN) logic. It further withstands all known attacks and performs well with respect to computational cost.
{"title":"Biometric based User Authentication Protocol for Mobile Cloud Environment","authors":"M. Vivekanandan, V. N. Sastry, U. S. Reddy","doi":"10.1109/ISBA.2019.8778529","DOIUrl":"https://doi.org/10.1109/ISBA.2019.8778529","url":null,"abstract":"Mobile user authentication is a challenging task in the mobile cloud computing (MCC). In 2015, Tsai and Lo’s developed authentication protocol in distributed MCC. Which is vulnerable to the biometric misuse, incorrect login credentials (password and fingerprint) and attacks for service provider impersonation. It has no provision for smart-card revocation and lacks mutual authentication. To address this-mentioned issues, we propose a novel Biometric based User Authentication Protocol for MCC. The proposed protocol supports session key agreement of participants and flawless mutual authentication. Our protocol is verified using Burrows-Abadi-Needham (BAN) logic. It further withstands all known attacks and performs well with respect to computational cost.","PeriodicalId":270033,"journal":{"name":"2019 IEEE 5th International Conference on Identity, Security, and Behavior Analysis (ISBA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130544260","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 : 2019-01-01DOI: 10.1109/ISBA.2019.8778581
E. Ribeiro, A. Uhl, F. Alonso-Fernandez
Several recent works have addressed the ability of deep learning to disclose rich, hierarchical and discriminative models for the most diverse purposes. Specifically in the super-resolution field, Convolutional Neural Networks (CNNs) using different deep learning approaches attempt to recover realistic texture and fine grained details from low resolution images. In this work we explore the viability of these approaches for iris Super-Resolution (SR) in an iris recognition environment. For this, we test different architectures with and without a so called image re-projection to reduce artifacts applying it to different iris databases to verify the viability of the different CNNs for iris super-resolution. Results show that CNNs and image re-projection can improve the results specially for the accuracy of recognition systems using a complete different training database performing the transfer learning successfully.
{"title":"Super-Resolution and Image Re-projection for Iris Recognition","authors":"E. Ribeiro, A. Uhl, F. Alonso-Fernandez","doi":"10.1109/ISBA.2019.8778581","DOIUrl":"https://doi.org/10.1109/ISBA.2019.8778581","url":null,"abstract":"Several recent works have addressed the ability of deep learning to disclose rich, hierarchical and discriminative models for the most diverse purposes. Specifically in the super-resolution field, Convolutional Neural Networks (CNNs) using different deep learning approaches attempt to recover realistic texture and fine grained details from low resolution images. In this work we explore the viability of these approaches for iris Super-Resolution (SR) in an iris recognition environment. For this, we test different architectures with and without a so called image re-projection to reduce artifacts applying it to different iris databases to verify the viability of the different CNNs for iris super-resolution. Results show that CNNs and image re-projection can improve the results specially for the accuracy of recognition systems using a complete different training database performing the transfer learning successfully.","PeriodicalId":270033,"journal":{"name":"2019 IEEE 5th International Conference on Identity, Security, and Behavior Analysis (ISBA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114614472","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 : 2019-01-01DOI: 10.1109/ISBA.2019.8778490
Priyanka Roy, Soumen Bag
Forgery activity in legal handwritten documents is an identifiable problem. Falsification of document due to minute alteration of existings not only causes immense financial loss to a person or to any organization but also lessens the economic growth of a country. Here, we introduce and present a solution to detect forgery in handwritten documents by analyzing perceptually similar ink of different pens. The research is all about forensic investigation of handwritten word alteration which is performed by adding extra letter in a way such that the whole meaning of the word changes. The problem is formulated as binary classification problem. If words of the corresponding document are written by same pen, these are classified as positive class and words of a document accompanied with little inclusion of letters as a forgery attack, are classified as negative class. The article proposes Multilayer Perceptron classifier which has been adopted to classify data instances that have been computed by extracting Y CbCr color-based statistical features. This proposal has been tested on data set which has been generated by 10 blue and 10 black ball point pens. The respective obtained average accuracy is 83.71% and 78. 18% for blue pen data and black pen data.
{"title":"Forensic Performance on Handwriting to Identify Forgery Owing to Word Alteration","authors":"Priyanka Roy, Soumen Bag","doi":"10.1109/ISBA.2019.8778490","DOIUrl":"https://doi.org/10.1109/ISBA.2019.8778490","url":null,"abstract":"Forgery activity in legal handwritten documents is an identifiable problem. Falsification of document due to minute alteration of existings not only causes immense financial loss to a person or to any organization but also lessens the economic growth of a country. Here, we introduce and present a solution to detect forgery in handwritten documents by analyzing perceptually similar ink of different pens. The research is all about forensic investigation of handwritten word alteration which is performed by adding extra letter in a way such that the whole meaning of the word changes. The problem is formulated as binary classification problem. If words of the corresponding document are written by same pen, these are classified as positive class and words of a document accompanied with little inclusion of letters as a forgery attack, are classified as negative class. The article proposes Multilayer Perceptron classifier which has been adopted to classify data instances that have been computed by extracting Y CbCr color-based statistical features. This proposal has been tested on data set which has been generated by 10 blue and 10 black ball point pens. The respective obtained average accuracy is 83.71% and 78. 18% for blue pen data and black pen data.","PeriodicalId":270033,"journal":{"name":"2019 IEEE 5th International Conference on Identity, Security, and Behavior Analysis (ISBA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116036581","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 : 2019-01-01DOI: 10.1109/ISBA.2019.8778470
K. Raja, Ramachandra Raghavendra, C. Busch
The need to protect biometric data within iris systems has resulted in a number of template protection schemes. A primary issue with current template protection schemes for iris recognition is the unavoidable biometric error rates, i.e., for any given False Non-Match Rate (FNMR) there is a high False Match Rate (FMR), especially at lower values of FNMR. In this work, we primarily focus on addressing this problem using a new approach with Cuckoo Filtering simultaneously using both stable bits and discriminative bits to derive a stronger template protection scheme. The proposed template protection scheme performs in a robust manner for various configurations as compared to earlier template protection schemes that need empirical fine-tuning. With the set of experiments on a publicly available iris dataset, we benchmark our results against the state-of-art template protection scheme based on Bloom-Filters. Specifically, we demonstrate the gain in performance and robustness of proposed approach at lower FNMR and invariance of performance to configurations of template protection scheme. With a specific configuration of proposed approach, we achieve Genuine Match Rate (GMR) = 100% at FMR = 0:01% and EER = 0% in the best case and GMR = 98:44% at FMR = 0:01% and EER = 0:33% in the worst case on IITD Iris database.
{"title":"Towards Reducing the Error Rates in Template Protection for Iris Recognition Using Custom Cuckoo Filters","authors":"K. Raja, Ramachandra Raghavendra, C. Busch","doi":"10.1109/ISBA.2019.8778470","DOIUrl":"https://doi.org/10.1109/ISBA.2019.8778470","url":null,"abstract":"The need to protect biometric data within iris systems has resulted in a number of template protection schemes. A primary issue with current template protection schemes for iris recognition is the unavoidable biometric error rates, i.e., for any given False Non-Match Rate (FNMR) there is a high False Match Rate (FMR), especially at lower values of FNMR. In this work, we primarily focus on addressing this problem using a new approach with Cuckoo Filtering simultaneously using both stable bits and discriminative bits to derive a stronger template protection scheme. The proposed template protection scheme performs in a robust manner for various configurations as compared to earlier template protection schemes that need empirical fine-tuning. With the set of experiments on a publicly available iris dataset, we benchmark our results against the state-of-art template protection scheme based on Bloom-Filters. Specifically, we demonstrate the gain in performance and robustness of proposed approach at lower FNMR and invariance of performance to configurations of template protection scheme. With a specific configuration of proposed approach, we achieve Genuine Match Rate (GMR) = 100% at FMR = 0:01% and EER = 0% in the best case and GMR = 98:44% at FMR = 0:01% and EER = 0:33% in the worst case on IITD Iris database.","PeriodicalId":270033,"journal":{"name":"2019 IEEE 5th International Conference on Identity, Security, and Behavior Analysis (ISBA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128728016","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 : 2018-12-18DOI: 10.1109/ISBA.2019.8778453
Avantika Singh, Gaurav Jaswal, A. Nigam
At present spoofing attacks via which biometric system is potentially vulnerable against a fake biometric characteristic, introduces a great challenge to recognition performance. Despite the availability of a broad range of presentation attack detection (PAD) or liveness detection algorithms, fingerprint sensors are vulnerable to spoofing via fake fingers. In such situations, finger dorsal images can be thought of as an alternative which can be captured without much user cooperation and are more appropriate for outdoor security applications. In this paper, we present a first feasibility study of spoofing attack scenarios on finger dorsal authentication system, which include four types of presentation attacks such as printed paper, wrapped printed paper, scan and mobile. This study also presents a CNN based spoofing attack detection method which employ state-of-the-art deep learning techniques along with transfer learning mechanism. We have collected 196 finger dorsal real images from 33 subjects, captured with a Lytro camera and also created a set of 784 finger dorsal spoofing images. Extensive experimental results have been performed that demonstrates the superiority of the proposed approach for various spoofing attacks.
{"title":"FDSNet: Finger dorsal image spoof detection network using light field camera","authors":"Avantika Singh, Gaurav Jaswal, A. Nigam","doi":"10.1109/ISBA.2019.8778453","DOIUrl":"https://doi.org/10.1109/ISBA.2019.8778453","url":null,"abstract":"At present spoofing attacks via which biometric system is potentially vulnerable against a fake biometric characteristic, introduces a great challenge to recognition performance. Despite the availability of a broad range of presentation attack detection (PAD) or liveness detection algorithms, fingerprint sensors are vulnerable to spoofing via fake fingers. In such situations, finger dorsal images can be thought of as an alternative which can be captured without much user cooperation and are more appropriate for outdoor security applications. In this paper, we present a first feasibility study of spoofing attack scenarios on finger dorsal authentication system, which include four types of presentation attacks such as printed paper, wrapped printed paper, scan and mobile. This study also presents a CNN based spoofing attack detection method which employ state-of-the-art deep learning techniques along with transfer learning mechanism. We have collected 196 finger dorsal real images from 33 subjects, captured with a Lytro camera and also created a set of 784 finger dorsal spoofing images. Extensive experimental results have been performed that demonstrates the superiority of the proposed approach for various spoofing attacks.","PeriodicalId":270033,"journal":{"name":"2019 IEEE 5th International Conference on Identity, Security, and Behavior Analysis (ISBA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124086575","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 : 2018-12-15DOI: 10.1109/ISBA.2019.8778623
Daksh Thapar, Gaurav Jaswal, A. Nigam, Vivek Kanhangad
Designing an end-to-end deep learning network to match the biometric features with limited training samples is an extremely challenging task. To address this problem, we propose a new way to design an end-to-end deep CNN framework i.e., PVSNet that works in two major steps: first, an encoder-decoder network is used to learn generative domain-specific features followed by a Siamese network in which convolutional layers are pre-trained in an unsupervised fashion as an autoencoder. The proposed model is trained via triplet loss function that is adjusted for learning feature embeddings in a way that minimizes the distance between embedding-pairs from the same subject and maximizes the distance with those from different subjects, with a margin. In particular, a triplet Siamese matching network using an adaptive margin based hard negative mining has been suggested. The hyper-parameters associated with the training strategy, like the adaptive margin, have been tuned to make the learning more effective on biometric datasets. In extensive experimentation, the proposed network outperforms most of the existing deep learning solutions on three type of typical vein datasets which clearly demonstrates the effectiveness of our proposed method.
{"title":"PVSNet: Palm Vein Authentication Siamese Network Trained using Triplet Loss and Adaptive Hard Mining by Learning Enforced Domain Specific Features","authors":"Daksh Thapar, Gaurav Jaswal, A. Nigam, Vivek Kanhangad","doi":"10.1109/ISBA.2019.8778623","DOIUrl":"https://doi.org/10.1109/ISBA.2019.8778623","url":null,"abstract":"Designing an end-to-end deep learning network to match the biometric features with limited training samples is an extremely challenging task. To address this problem, we propose a new way to design an end-to-end deep CNN framework i.e., PVSNet that works in two major steps: first, an encoder-decoder network is used to learn generative domain-specific features followed by a Siamese network in which convolutional layers are pre-trained in an unsupervised fashion as an autoencoder. The proposed model is trained via triplet loss function that is adjusted for learning feature embeddings in a way that minimizes the distance between embedding-pairs from the same subject and maximizes the distance with those from different subjects, with a margin. In particular, a triplet Siamese matching network using an adaptive margin based hard negative mining has been suggested. The hyper-parameters associated with the training strategy, like the adaptive margin, have been tuned to make the learning more effective on biometric datasets. In extensive experimentation, the proposed network outperforms most of the existing deep learning solutions on three type of typical vein datasets which clearly demonstrates the effectiveness of our proposed method.","PeriodicalId":270033,"journal":{"name":"2019 IEEE 5th International Conference on Identity, Security, and Behavior Analysis (ISBA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114748164","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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