2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE)最新文献

Improving the convergence of co-training for audio-visual person identification 提高视听人识别协同训练的收敛性

2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE) Pub Date : 2016-07-06 DOI: 10.1109/SPLIM.2016.7528400

Nicolai Bæk Thomsen, Xiaodong Duan, Z. Tan, B. Lindberg, S. H. Jensen

{"title":"Improving the convergence of co-training for audio-visual person identification","authors":"Nicolai Bæk Thomsen, Xiaodong Duan, Z. Tan, B. Lindberg, S. H. Jensen","doi":"10.1109/SPLIM.2016.7528400","DOIUrl":"https://doi.org/10.1109/SPLIM.2016.7528400","url":null,"abstract":"Person identification is a very important task for intelligent devices when communicating or interacting with humans. A potential problem in real applications is that the amount of enrollment data is insufficient. When multiple modalities are available, it is possible to re-train the system online by exploiting the conditional independence between the modalities and thus improving classification accuracy. This can be achieved by the well-known CO-training algorithm [1]. In this work we present a novel modification to the CO-training algorithm, which is concerned with how new observations/samples are chosen at each iteration to re-train the system in order to improve the classification accuracy faster, i.e., better convergence. In our method, the new data are chosen not only based on the score from the other modality but also the score from the self modality. We demonstrate our proposed method on a multimodal person identification task using the MOBIO database, and show that it outperforms the baseline method, in terms of convergency, by a large margin.","PeriodicalId":297318,"journal":{"name":"2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116745791","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

Towards neural art-based face de-identification in video data 基于神经艺术的视频数据人脸去识别研究

2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE) Pub Date : 2016-07-06 DOI: 10.1109/SPLIM.2016.7528406

K. Brkić, T. Hrkać, I. Sikirić, Z. Kalafatić

引用次数: 6

GMM-based speaker gender and age classification after voice conversion 语音转换后基于gmm的说话人性别和年龄分类

2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE) Pub Date : 2016-07-06 DOI: 10.1109/SPLIM.2016.7528391

J. Pribil, A. Přibilová, J. Matoušek

引用次数: 13

Cycled merging registration of point clouds for 3D human body modeling 三维人体建模中点云的循环合并配准

2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE) Pub Date : 2016-07-06 DOI: 10.1109/SPLIM.2016.7528394

Yanjie Chen, Yuhong Li, F. Qi, Zhanyu Ma, Honggang Zhang

引用次数: 7

Cancelable biometrics for finger vein recognition 可取消的生物识别手指静脉

2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE) Pub Date : 2016-07-06 DOI: 10.1109/SPLIM.2016.7528396

Emanuela Piciucco, E. Maiorana, Christof Kauba, A. Uhl, P. Campisi

引用次数: 26

Effect of multi-condition training and speech enhancement methods on spoofing detection 多条件训练和语音增强方法对欺骗检测的影响

2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE) Pub Date : 2016-07-06 DOI: 10.1109/SPLIM.2016.7528399

Hong Yu, A. K. Sarkar, Dennis Alexander Lehmann Thomsen, Z. Tan, Zhanyu Ma, Jun Guo

{"title":"Effect of multi-condition training and speech enhancement methods on spoofing detection","authors":"Hong Yu, A. K. Sarkar, Dennis Alexander Lehmann Thomsen, Z. Tan, Zhanyu Ma, Jun Guo","doi":"10.1109/SPLIM.2016.7528399","DOIUrl":"https://doi.org/10.1109/SPLIM.2016.7528399","url":null,"abstract":"Many researchers have demonstrated the good performance of spoofing detection systems under clean training and testing conditions. However, it is well known that the performance of speaker and speech recognition systems significantly degrades in noisy conditions. Therefore, it is of great interest to investigate the effect of noise on the performance of spoofing detection systems. In this paper, we investigate a multi-conditional training method where spoofing detection models are trained with a mix of clean and noisy data. In addition, we study the effect of different noise types as well as speech enhancement methods on a state-of-the-art spoofing detection system based on the dynamic linear frequency cepstral coefficients (LFCC) feature and a Gaussian mixture model maximum-likelihood (GMM-ML) classifier. In the experiment part we consider three additive noise types, Cantine, Babble and white Gaussian at different signal-to-noise ratios, and two mainstream speech enhancement methods, Wiener filtering and minimum mean-square error. The experimental results show that enhancement methods are not suitable for the spoofing detection task, as the spoofing detection accuracy will be reduced after speech enhancement. Multi-conditional training, however, shows potential at reducing error rates for spoofing detection.","PeriodicalId":297318,"journal":{"name":"2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124686278","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: 37

Piecewise linear definition of transformation functions for speaker de-identification 说话人去识别变换函数的分段线性定义

2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE) Pub Date : 2016-07-06 DOI: 10.1109/SPLIM.2016.7528408

Carmen Magariños, Paula Lopez-Otero, Laura Docío Fernández, E. R. Banga, C. García-Mateo, D. Erro

引用次数: 14

Employing speech and location information for automatic assessment of child language environments 使用语音和位置信息来自动评估儿童语言环境

2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE) Pub Date : 2016-07-06 DOI: 10.1109/SPLIM.2016.7528412

M. Najafian, Dwight W. Irvin, Ying Luo, B. Rous, J. Hansen

引用次数: 11

Efficient fingerprint image protection principles using selective JPEG2000 encryption 采用选择性JPEG2000加密的高效指纹图像保护原理

2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE) Pub Date : 2016-07-06 DOI: 10.1109/SPLIM.2016.7528392

Martin Draschl, Jutta Hämmerle-Uhl, A. Uhl

引用次数: 5

Kernel subclass support vector description for face and human action recognition 核子类支持向量描述用于人脸和人体动作识别

2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE) Pub Date : 2016-07-06 DOI: 10.1109/SPLIM.2016.7528409

V. Mygdalis, Alexandros Iosifidis, A. Tefas, I. Pitas

引用次数: 7