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Dynamic Feature Queue for Surveillance Face Anti-spoofing via Progressive Training

Dynamic Feature Queue for Surveillance Face Anti-spoofing vi...

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2023 IEEE/CVF Conference on computer vision and pattern recognition Workshops, CVPRW 2023

作者： Wang, Keyao Huang, Mouxiao Zhang, Guosheng Yue, Haixiao Zhang, Gang Qiao, Yu China Chinese Academy of Sciences ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology China University of Chinese Academy of Sciences China

ISBN: (纸本)9798350302493

In recent years, face recognition systems have faced increasingly security threats, making it essential to employ Face Anti-spoofing (FAS) to protect against various types of attacks in traditional scenarios like phone unlocking, face payment and self-service security inspection. However, further exploration is required to fully secure FAS in long-distance settings. In this paper, we propose two contributions to enhance the security of face recognition systems: Dynamic Feature Queue (DFQ) and Progressive Training Strategy (PTS). DFQ converts the conventional binary classification task into a multi-classification task. It treats live samples as a closed set and attack samples as an open set by using a dynamic queue that stores the features of spoofing samples and updates them. On the other hand, PTS targets difficult samples and iteratively adds them in batches for training. The proposed PTS divides the entire training set into blocks, trains only a small portion of the data, and gradually increases the training data with each stage while also incorporating low-scoring positive samples and high-scoring spoof samples from the test set. These two contributions complement each other by enhancing the model's ability to generalize and defend against various types of attacks, making the face recognition system more secure and reliable. Our proposed methods have achieved top performance on ACER metric with 4.73% on the SuHiFiMask dataset [11] and won the first prize in Surveillance Face Anti-spoofing track of the Challenge@CVPR 2023. © 2023 IEEE.

关键词： Iterative methods

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A New Contrastive Learning Based Model for Estimating Degree of Multiple Personality Traits Using Social Media Posts 1

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7th Asian Conference on pattern recognition, ACPR 2023

作者： Biswas, Kunal Shivakumara, Palaiahnakote Pal, Umapada Sarkar, Ram Jadavpur University Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kula Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (数字)9783031476372

ISBN: (纸本)9783031476365

Estimating the degree of multiple personality traits in a single image is challenging due to the presence of multiple people, occlusion, poor quality etc. Unlike existing methods which focus on the classification of a single personality using images, this work focuses on estimating different personality traits using a single image. We believe that when the image contains multiple persons and modalities, one can expect multiple emotions and expressions. This work separates given input images into different faces of people, recognized text, meta-text and background information using face segmentation, text recognition and scene detection techniques. Contrastive learning is explored to extract features from each segmented region based on clustering. The proposed work fuses textual and visual features extracted from the image for estimating the degree of multiple personality traits. Experimental results on our benchmark datasets show that the proposed model is effective and outperforms the existing methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Image segmentation

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Classification of gothic and baroque architectural elements

Classification of gothic and baroque architectural elements

引用

2012 19th International Conference on Systems, Signals and Image Processing, IWSSIP 2012

作者： Shalunts, Gayane Haxhimusa, Yll Sablatnig, Robert Institute of Computer Aided Automation Computer Vision Lab. Vienna University of Technology Austria Institute of Computer Graphics and Algorithms Pattern Recognition and Image Processing Lab. Vienna University of Technology Austria

ISBN: (纸本)9783200023284

Architectural elements are the components and details of buildings. Their unique set, combination, design, construction technique form the architectural style of buildings. Building facade classification by architectural styles is viewed as a task of classifying separate architectural structural elements. In the scope of building facade architectural style classification the current paper targets the problem of classification of Gothic and Baroque architectural elements called tracery, pediment and balustrade. Since certain gradient directions dominate on the shape of each architectural element, discrimination between dominating gradients means classification of architectural elements and thus architectural styles. We use local features to describe gradient directions. Our approach is based on clustering and learning of local features and yields a high classification rate. © 2012 Institute of Telecommunica.

关键词： Classification (of information)

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Region-based image annotation using asymmetrical support vector machine-based multiple-instance learning

Region-based image annotation using asymmetrical support vec...

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2006 IEEE computer Society Conference on computer vision and pattern recognition, CVPR 2006

作者： Yang, Changbo Dong, Ming Hua, Jing Machine Vision and Pattern Recognition Lab. Department of Computer Science Wayne State University Detroit MI 48202 Department of Computer Science Wayne State University Detroit MI 48202

ISBN: (纸本)0769525970

In region-based image annotation, keywords are usually associated with images instead of individual regions in the training data set. This poses a major challenge for any learning strategy. In this paper, we formulate image annotation as a supervised learning problem under Multiple-Instance Learning (MIL) framework. We present a novel Asymmetrical Support Vector Machine-based MIL algorithm (ASVM-MIL), which extends the conventional Support Vector Machine (SVM) to the MIL setting by introducing asymmetrical loss functions for false positives and false negatives. The proposed ASVM-MIL algorithm is evaluated on both image annotation data sets and the benchmark MUSK data sets. © 2006 IEEE.

关键词： Image processing

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A new method for arbitrarily-oriented text detection in video

A new method for arbitrarily-oriented text detection in vide...

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10th IAPR International Workshop on Document Analysis Systems, DAS 2012

作者： Sharma, Nabin Shivakumara, Palaiahnakote Pal, Umapada Blumenstein, Michael Tan, Chew Lim Griffith University QLD Australia School of Computing National University of Singapore Singapore Singapore Computer Vision and Pattern Recognition Unit Indian Statistical Unit Kolkata India

ISBN: (纸本)9780769546612

Text detection in video frames plays a vital role in enhancing the performance of information extraction systems because the text in video frames helps in indexing and retrieving video efficiently and accurately. This paper presents a new method for arbitrarily-oriented text detection in video, based on dominant text pixel selection, text representatives and region growing. The method uses gradient pixel direction and magnitude corresponding to Sobel edge pixels of the input frame to obtain dominant text pixels. Edge components in the Sobel edge map corresponding to dominant text pixels are then extracted and we call them text representatives. We eliminate broken segments of each text representatives to get candidate text representatives. Then the perimeter of candidate text representatives grows along the text direction in the Sobel edge map to group the neighboring text components which we call word patches. The word patches are used for finding the direction of text lines and then the word patches are expanded in the same direction in the Sobel edge map to group the neighboring word patches and to restore missing text information. This results in extraction of arbitrarily-oriented text from the video frame. To evaluate the method, we considered arbitrarily-oriented data, non-horizontal data, horizontal data, Hua's data and ICDAR-2003 competition data (Camera images). The experimental results show that the proposed method outperforms the existing method in terms of recall and f-measure. © 2012 IEEE.

关键词： Pixels

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Off-line Bangla signature verification

Off-line Bangla signature verification

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10th IAPR International Workshop on Document Analysis Systems, DAS 2012

作者： Pal, Srikanta Nguyen, Vu Blumenstein, Michael Pal, Umapada School of Information and Communication Technology Griffith University Gold Coast QLD Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata-700108 India

ISBN: (纸本)9780769546612

In the field of information security, biometric systems play an important role. Within biometrics, automatic signature identification and verification has been a strong research area because of the social and legal acceptance and extensive use of the written signature as an individual authentication. Signature verification is a process in which the questioned signature is examined in detail in order to determine whether it belongs to the claimed person or not. Despite substantial research in the field of signature verification involving Western signatures, very few works have been dedicated to non-Western signatures such as Chinese, Japanese, Arabic, or Persian etc. In this paper, the performance of an off-line signature verification system involving Bangla signatures, whose style is distinct from Western scripts, was investigated. The Gaussian Grid feature extraction technique was employed for feature extraction and Support Vector Machines (SVMs) were considered for classification. The Bangla signature database employed in the experiments consisted of 3000 forgeries and 2400 genuine signatures. An encouraging accuracy of 90.4% was obtained from the experiments. © 2012 IEEE.

关键词： Biometrics

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Comparative study of Devnagari handwritten character recognition using different feature and classifiers

Comparative study of Devnagari handwritten character recogni...

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ICDAR2009 - 10th International Conference on Document Analysis and recognition

作者： Pal, U. Wakabayashi, T. Kimura, F. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata-108 India Graduate School of Engineering Mie University TSU Mie 514-8507 Japan

ISBN: (纸本)9780769537252

In recent years research towards Indian handwritten character recognition is getting increasing attention. Many approaches have been proposed by the researchers towards handwritten Indian character recognition and many recognition systems for isolated handwritten numerals/characters are available in the literature. To get idea of the recognition results of different classifiers and to provide new benchmark for future research, in this paper a comparative study of Devnagari handwritten character recognition using twelve different classifiers and four sets of feature is presented. Projection distance, subspace method, linear discriminant function, support vector machines, modified quadratic discriminant function, mirror image learning, Euclidean distance, nearest neighbour, k-Nearest neighbour, modified projection distance, compound projection distance, and compound modified quadratic discriminant function are used as different classifiers. Feature sets used in the classifiers are computed based on curvature and gradient information obtained from binary as well as gray-scale images. © 2009 IEEE.

关键词： Character recognition

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Pectoral muscle detection in digital breast tomosynthesis and mammography

Pectoral muscle detection in digital breast tomosynthesis an...

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3rd International MICCAI Workshop on Medical computer vision, MCV 2013

作者： Ghesu, Florin C. Wels, Michael Jerebko, Anna Sühling, Michael Hornegger, Joachim Kelm, B. Michael Siemens Corporate Technology Imaging and Computer Vision Erlangen Germany Pattern Recognition Department Friedrich Alexander University Erlangen Germany Siemens AG Healthcare Erlangen Germany

ISBN: (纸本)9783319055299

Screening and diagnosis of breast cancer with Digital Breast Tomosynthesis (DBT) and Mammography are increasingly supported by algorithms for automatic post-processing. The pectoral muscle, which dorsally delineates the breast tissue towards the chest wall, is an important anatomical structure for navigation. Along with the nipple and the skin, the pectoral muscle boundary is often used for reporting the location of breast lesions. It is visible in mediolateral oblique (MLO) views where it is well approximated by a straight line. Here, we propose two machine learning-based algorithms to robustly detect the pectoral muscle in MLO views from DBT and mammography. Embedded into the Marginal Space Learning framework, the algorithms involve the evaluation of multiple candidate boundaries in a hierarchical manner. To this end, we propose a novel method for candidate generation using a Hough-based approach. Experiments were performed on a set of 100 DBT volumes and 95 mammograms from different clinical cases. Our novel combined approach achieves competitive accuracy and robustness. In particular, for the DBT data, we achieve significantly lower deviation angle error and mean distance error than the standard approach. The proposed algorithms run within a few seconds. © 2014 Springer International Publishing Switzerland.

关键词： Learning algorithms

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Online handwritten Indian script recognition: A human motor function based framework

Online handwritten Indian script recognition: A human motor ...

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作者： Garain, U. Chaudhuri, B.B. Pal, T.T. Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Kolkata 700 108 India

This paper presents the online handwriting recognition for Indian scripts. The primary concern of the approach is the modeling of human motor functionality while writing characters. This is achieved by looking at the whole pen trajectory where the time evaluation of the pen coordinates plays a crucial role. A low complexity classifier has been designed and the proposed similarity measure appears to be quite robust against wide variations in writing styles. Initially, the approach has been applied for online recognition of handwritten characters in Devnagari and Bangla, the two major Indian scripts. A test on a dataset of considerable size shows promising recognition rates namely, 97.29% for Devnagari and 96.34% for Bangla. © 2002 IEEE.

关键词： pattern recognition

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Text independent writer identification for Bengali script

Text independent writer identification for Bengali script

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International Conference on pattern recognition

作者： Chanda, Sukalpa Franke, Katrin Pal, Umapada Wakabayashi, Tetsushi Department of Computer Science and Media Technology Gjøvik University College Norway Computer Vision and Pattern Recognition Unit Indian Statistical Institute India Graduate School of Engineering Mie University Japan

ISBN: (纸本)9780769541099

Automatic identification of an individual based on his/her handwriting characteristics is an important forensic tool. In a computational forensic scenario, presence of huge amount of text/information in a questioned document cannot be always ensured. Also, compromising in terms of systems reliability under such situation is not desirable. We here propose a system to encounter such adverse situation in the context of Bengali script. Experiments with discrete directional feature and gradient feature are reported here, along with Support Vector Machine (SVM) as classifier. We got promising results of 95.19% writer identification accuracy at first top choice and 99.03% when considering first three top choices. © 2010 IEEE.

关键词： Support vector machines

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