2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA)最新文献

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Local feature hierarchy for face recognition across pose and illumination 基于姿态和光照的局部特征层次识别
Xiaoyue Jiang, Dong Zhang, Xiaoyi Feng
{"title":"Local feature hierarchy for face recognition across pose and illumination","authors":"Xiaoyue Jiang, Dong Zhang, Xiaoyi Feng","doi":"10.1109/IPTA.2016.7821023","DOIUrl":"https://doi.org/10.1109/IPTA.2016.7821023","url":null,"abstract":"Even though face recognition in frontal view and normal lighting condition works very well, the performance degenerates sharply in extreme conditions. In real applications, both the lighting and pose variation will always be encountered at the same time. Accordingly we propose an end-to-end face recognition method to deal with pose and illumination simultaneously based on convolutional neural networks where the discriminative nonlinear features that are invariant to pose and illumination are extracted. Normally the global structure for images taken in different views is quite diverse. Therefore we propose to use the 1 × 1 convolutional kernel to extract the local features. Furthermore the parallel multi-stream multi-layer 1 × 1 convolution network is developed to extract multi-hierarchy features. In the experiments we obtained the average face recognition rate of 96.9% on multiPIE dataset, which improves the state-of-the-art of face recognition across poses and illumination by 7.5%. Especially for profile-wise positions, the average recognition rate of our proposed network is 97.8%, which increases the state-of-the-art recognition rate by 19%.","PeriodicalId":123429,"journal":{"name":"2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129203802","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: 2
Unsupervised deep hashing for large-scale visual search 大规模视觉搜索的无监督深度哈希
Zhaoqiang Xia, Xiaoyi Feng, Jinye Peng, A. Hadid
{"title":"Unsupervised deep hashing for large-scale visual search","authors":"Zhaoqiang Xia, Xiaoyi Feng, Jinye Peng, A. Hadid","doi":"10.1109/IPTA.2016.7821007","DOIUrl":"https://doi.org/10.1109/IPTA.2016.7821007","url":null,"abstract":"Learning based hashing plays a pivotal role in large-scale visual search. However, most existing hashing algorithms tend to learn shallow models that do not seek representative binary codes. In this paper, we propose a novel hashing approach based on unsupervised deep learning to hierarchically transform features into hash codes. Within the heterogeneous deep hashing framework, the autoencoder layers with specific constraints are considered to model the nonlinear mapping between features and binary codes. Then, a Restricted Boltzmann Machine (RBM) layer with constraints is utilized to reduce the dimension in the hamming space. The experiments on the problem of visual search demonstrate the competitiveness of our proposed approach compared to the state of the art.","PeriodicalId":123429,"journal":{"name":"2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125113365","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: 23
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