2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)最新文献_第2页

Pre-trained classifiers with One Shot Similarity for context aware face verification and identification 上下文感知人脸验证和识别的一次性相似性预训练分类器

2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA) Pub Date : 2017-02-01 DOI: 10.1109/ISBA.2017.7947687

Monika Sharma, R. Hebbalaguppe, L. Vig

{"title":"Pre-trained classifiers with One Shot Similarity for context aware face verification and identification","authors":"Monika Sharma, R. Hebbalaguppe, L. Vig","doi":"10.1109/ISBA.2017.7947687","DOIUrl":"https://doi.org/10.1109/ISBA.2017.7947687","url":null,"abstract":"Most affect based systems analyse facial expressions for emotion detection, and utilize face detection and recognition methods in order to do effective affect analysis. Recent work has demonstrated the efficacy of deep architectures for face recognition by training as classifiers on voluminous datasets. Some architectures are trained as classifiers, and some directly learn an embedding via a triplet loss function. In this paper, we consider the case of one shot prediction from the feature space learnt initially via classification, i.e. we consider the situation where we have a pre-trained model, but do not have access to the training data and are required to make predictions on novel faces with just one training image per identity. We utilize the one shot similarity metric in order to compute similarity scores and compare it with the state-of-the-art results on the Youtube videos face dataset (YTF). We demonstrate the effect of temporal context on frame wise face recognition, and use a probabilistic majority voting scheme over past frames to determine current frame identity. Additionally, we found a number of labelling errors in the Youtube face dataset that were not published in the errata, and have published the same online for the benefit of the community.","PeriodicalId":436086,"journal":{"name":"2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121493751","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}

引用次数: 1

Anatomy of secondary features in keystroke dynamics - achieving more with less 解剖在击键动力学次要特征-实现更少

2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA) Pub Date : 2017-02-01 DOI: 10.1109/ISBA.2017.7947691

Yan Lindsay Sun, Hayreddin Çeker, S. Upadhyaya

引用次数: 8

BioSoft - a multimodal biometric database incorporating soft traits BioSoft -一个多模态生物特征数据库，包含软特征

2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA) Pub Date : 2017-02-01 DOI: 10.1109/ISBA.2017.7947693

Debanjan Sadhya, Parth Pahariya, Rishi Yadav, Apoorv Rastogi, Ayush Kumar, Lakshya Sharma, S. Singh

{"title":"BioSoft - a multimodal biometric database incorporating soft traits","authors":"Debanjan Sadhya, Parth Pahariya, Rishi Yadav, Apoorv Rastogi, Ayush Kumar, Lakshya Sharma, S. Singh","doi":"10.1109/ISBA.2017.7947693","DOIUrl":"https://doi.org/10.1109/ISBA.2017.7947693","url":null,"abstract":"Biometrics is an automated authentication mechanism that allows the identification or verification of individuals based on unique physiological and behavioral characteristics. In addition to novel biometric recognition frameworks and protocols, standard databases containing sample biometric traits are essential for validating the obtained results. In this paper, we introduce a new multimodal database named BioSoft which consists of biometric data collected from 75 individuals. In comparison to the already existing databases, BioSoft contains a set of 23 soft biometric traits corresponding to each enrolled individual. This property makes our database very useful due to the unavailability of any other manually extracted multimodal database incorporating soft biometric characteristics. Additionally, the primary biometric modalities of face, ear, iris, voice, handwriting and fingerprints (obtained from two different sensors) are present in this database. Thus our database contains both physiological and behavioral characteristics of individuals, thus making it applicable for validating a wide variety of approaches.","PeriodicalId":436086,"journal":{"name":"2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133688571","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}

引用次数: 7

Multi-object trajectory coupling using online target specific decision making 基于在线目标特定决策的多目标轨迹耦合

2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA) Pub Date : 2017-02-01 DOI: 10.1109/ISBA.2017.7947702

Tapas Badal, N. Nain, Mushtaq Ahmed

引用次数: 4

Modeling interactive sensor-behavior with smartphones for implicit and active user authentication 为智能手机的隐式和主动用户身份验证建模交互式传感器行为

2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA) Pub Date : 2017-02-01 DOI: 10.1109/ISBA.2017.7947694

Yufei Chen, Chao Shen, Zhao Wang, Tianwen Yu

{"title":"Modeling interactive sensor-behavior with smartphones for implicit and active user authentication","authors":"Yufei Chen, Chao Shen, Zhao Wang, Tianwen Yu","doi":"10.1109/ISBA.2017.7947694","DOIUrl":"https://doi.org/10.1109/ISBA.2017.7947694","url":null,"abstract":"While the public enjoy the convenience aroused by the proliferation of the smartphones, they also face the risk of exposing their sensitive and secure information to attackers. Extant smartphone authentication methods (e.g., PIN and fingerprint) typically provide one-time identity verification, but the verified user is still subject to session hijacking or masquerading attacks. In this paper, we propose a framework and performance analysis of using onboard-sensor behavior for continuous user authentication on smartphones, which can implicitly and continuously verifies the presence of a smartphone user. When a user carries the smartphone to do daily activities, time-, frequency- and wavelet-domain features are extracted from smartphone sensor data for accurately depicting users' motion patterns. A decision procedure based on one-class learning algorithms is developed and employed in the feature space to perform the continuous authentication task. Analyses are conducted based on sensor-interaction data on five typical daily activities with 27,681 samples across five phonecarrying positions. Extensive experiments in two specific scenarios are included to examine the efficacy of the proposed approach, which achieves a relatively high accuracy with the equal-error rate achieves 2.40% and 5.50% respectively. Our authentication system can be seamlessly integrated with extant smartphone authentication mechanisms, and is nonintrusive to users and does not need extra hardware.","PeriodicalId":436086,"journal":{"name":"2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127626966","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}

引用次数: 6

A hybrid model for CFA interpolation detection 一种CFA插值检测的混合模型

2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA) Pub Date : 2017-02-01 DOI: 10.1109/ISBA.2017.7947705

H. Verma, Arunav Saikia, N. Khanna

引用次数: 1

Passive fingerprinting for wireless devices: A multi-level decision approach 无线设备无源指纹识别:多层次决策方法

2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA) Pub Date : 2017-02-01 DOI: 10.1109/ISBA.2017.7947689

Chao Shen, Ruiyuan Lu, Saeid Samizade, Liang He

引用次数: 4

Pore based indexing for High-Resolution Fingerprints 基于孔隙的高分辨率指纹索引

2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA) Pub Date : 2017-02-01 DOI: 10.1109/ISBA.2017.7947685

V. Anand, Vivek Kanhangad

引用次数: 7

LivDet-Iris 2015 - Iris Liveness Detection Competition 2015 2015虹膜活体检测大赛

2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA) Pub Date : 2017-02-01 DOI: 10.1109/ISBA.2017.7947701

David Yambay, Brian Walczak, S. Schuckers, A. Czajka

{"title":"LivDet-Iris 2015 - Iris Liveness Detection Competition 2015","authors":"David Yambay, Brian Walczak, S. Schuckers, A. Czajka","doi":"10.1109/ISBA.2017.7947701","DOIUrl":"https://doi.org/10.1109/ISBA.2017.7947701","url":null,"abstract":"Presentation attacks such as printed iris images or patterned contact lenses can be used to circumvent an iris recognition system. Different solutions have been proposed to counteract this vulnerability with Presentation Attack Detection (commonly called liveness detection) being used to detect the presence of an attack, yet independent evaluations and comparisons are rare. To fill this gap we have launched the first international iris liveness competition in 2013. This paper presents detailed results of its second edition, organized in 2015 (LivDet-Iris 2015). Four software-based approaches to Presentation Attack Detection were submitted. Results were tallied across three different iris datasets using a standardized testing protocol and large quantities of live and spoof iris images. The Federico Algorithm received the best results with a rate of rejected live samples of 1.68% and rate of accepted spoof samples of 5.48%. This shows that simple static attacks based on paper printouts and printed contact lenses are still challenging to be recognized purely by software-based approaches. Similar to the 2013 edition, printed iris images were easier to be differentiated from live images in comparison to patterned contact lenses.","PeriodicalId":436086,"journal":{"name":"2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116583855","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}

引用次数: 47

Quasi-straightness based features for off-line verification of signatures 基于准直线度特征的离线签名验证

2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA) Pub Date : 2017-02-01 DOI: 10.1109/ISBA.2017.7947708

Md. Ajij, Sanjoy Pratihar

引用次数: 0