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

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A discriminative spatio-temporal mapping of face for liveness detection 一种用于活体检测的人脸判别时空映射

2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA) Pub Date : 2017-02-01 DOI: 10.1109/ISBA.2017.7947707

Nagashri N. Lakshminarayana, N. Narayan, N. Napp, S. Setlur, V. Govindaraju

{"title":"A discriminative spatio-temporal mapping of face for liveness detection","authors":"Nagashri N. Lakshminarayana, N. Narayan, N. Napp, S. Setlur, V. Govindaraju","doi":"10.1109/ISBA.2017.7947707","DOIUrl":"https://doi.org/10.1109/ISBA.2017.7947707","url":null,"abstract":"The proposed system aims to boost the performance of a face anti-spoofing system by fusing pulse based features with other spatial and temporal information that markedly define liveness. Most face recognition systems do not have an effective spoof detection module and hence are vulnerable to spoofing attacks. We address the above problem by developing a spatio-temporal mapping of face and then using a deep Convolutional Neural Network (CNN) to learn discriminative features for liveness detection. CNNs can act directly on the raw inputs, thus automating the process of feature construction. Instead of only relying on the deep CNN to learn features by skimming through all the frames of a sequence, a compact representation of face that captures only the selective features is given as an input. Features are extracted from both spatial and temporal dimensions through spectral analysis, thereby capturing the motion and physiological information encoded in multiple adjacent frames. The developed model generates multiple channels of information from the input frames, and the final feature representation is obtained by combining information from all channels. Our model differs from other models in this aspect. Our system is evaluated on two challenging databases, CASIA [27] and Replay-Attack [7], and the achieved results are presented in this paper. This work shows that the proposed model outperforms state-of-the-art methods on CASIA, achieves comparable result on REPLAY-ATTACK and reduces model complexity by exploiting few key features of liveness.","PeriodicalId":436086,"journal":{"name":"2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)","volume":"21 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":"116378894","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: 25

Unusual human activity detection using Markov Logic Networks 利用马尔可夫逻辑网络进行异常人类活动检测

2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA) Pub Date : 2017-02-01 DOI: 10.1109/ISBA.2017.7947700

Aditi Kapoor, K. K. Biswas, M. Hanmandlu

引用次数: 3

De-genderization by body contours reshaping 通过重塑身体轮廓来去性别化

2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA) Pub Date : 2017-02-01 DOI: 10.1109/ISBA.2017.7947709

Natacha Ruchaud, J. Dugelay

{"title":"De-genderization by body contours reshaping","authors":"Natacha Ruchaud, J. Dugelay","doi":"10.1109/ISBA.2017.7947709","DOIUrl":"https://doi.org/10.1109/ISBA.2017.7947709","url":null,"abstract":"This paper deals with privacy protection in video surveillance. More specifically, the main goal of this work is to make the gender of people no more recognizable while preserving enough information concerning body shape and motion of people for action classification. We denote this processing as de-genderization. Regarding the current state-of-art methods, most of them have privacy filters only dedicated to de-identify people. These methods do not automatically imply the suppression of visual semantic traits such as gender. Therefore, we propose two approaches that modify the visual appearance of the body shape in order to de-genderize people while keeping the possibility to interpret the video. In both methods we start by extracting the contour points attached to the body shape of people in videos. Then we either mix the coordinates of the body shape and a predefined model, or we smooth the body shape by successive polygonal approximations based on convexity. Our results demonstrate that both proposed approaches protect the gender information while preserving the global body movement. The second approach based on convexity better preserves the visibility of human activities.","PeriodicalId":436086,"journal":{"name":"2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)","volume":"13 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":"127736950","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: 3

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