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Person Identification in Smart Surveillance Robots using Sparse Interest Points

Procedia Computer Science 2018年 133卷 812-822页

作者： Vinay A Sai Krishna B V Manoj P N Nishanth A Rao K N Balasubramanya Muthy S Natarajan Center for Pattern Recognition and Machine Intelligence PES University 100 Feet Ring Road Banashankari Stage III Bengaluru Karnataka 560085 India

Face recognition presents a challenging and interesting problem in the field of robotics and computer vision. Robot systems which support face recognition has been vastly used for surveillance, defense etc., Robots prominently rely on real time feedbacks from sensors. Robots with face recognition have numerous applications such as automation process, object detection, security and surveillance, defense, autonomous vehicles etc. A robot face recognition system is a computer application used to automatically identify or verify a person from a digital image or a video frame from a video source. This is usually achieved through a comparison of selected facial features from an image and a facial database. In order to recognize a face in real time the images captured by camera have to be stored and then processed by face recognition algorithm. In the entire face recognition process, the choice of feature extractor is very important. The constraint on the feature extractor limits the accuracy. SIFT, SURF and ORB are amongst the prominent feature extractors due to their insusceptibility to constraints such as illumination, pose and scale. In this paper, we demonstrate that proposed SRP model with ORB gives better percent accuracy of around 85% after preprocessing.

关键词： Face recognition Feature Matching Sparse Random Projection SIFT SURF ORB FLANN

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The INTERSPEECH 2018 computational paralinguistics challenge: Atypical & self-assessed affect, crying & Heart beats 19

The INTERSPEECH 2018 computational paralinguistics challenge...

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19th Annual Conference of the International Speech Communication, INTERSPEECH 2018

作者： Schuller, Björn W. Steidl, Stefan Batliner, Anton Marschik, Peter B. Baumeister, Harald Dong, Fengquan Hantke, Simone Pokorny, Florian B. Rathner, Eva-Maria Bartl-Pokorny, Katrin D. Einspieler, Christa Zhang, Dajie Baird, Alice Amiriparian, Shahin Qian, Kun Ren, Zhao Schmitt, Maximilian Tzirakis, Panagiotis Zafeiriou, Stefanos GLAM - Group on Language Audio and Music Imperial College London United Kingdom ZD.B Department of Embedded Intelligence for Health Care and Wellbeing University of Augsburg Germany audEERING GmbH Gilching Germany Pattern Recognition Lab FAU Erlangen-Nuremberg Germany iDN - interdisciplinary Developmental Neuroscience Medical University of Graz Austria University Medical Center Göttingen Germany Department of Women's and Children's Health Karolinska Institutet Stockholm Sweden Department of Clinical Psychology and Psychotherapy University of Ulm Germany Shenzhen University General Hospital Shenzhen China Machine Intelligence and Signal Processing Group Technische Universität München Germany University of Oulu Finland

The INTERSPEECH 2018 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the Atypical Affect Sub-Challenge, four basic emotions annotated in the speech of handicapped subjects have to be classified;in the Self-Assessed Affect Sub-Challenge, valence scores given by the speakers themselves are used for a three-class classification problem;in the Crying Sub-Challenge, three types of infant vocalisations have to be told apart;and in the Heart Beats Sub-Challenge, three different types of heart beats have to be determined. We describe the Sub-Challenges, their conditions, and baseline feature extraction and classifiers, which include data-learnt (supervised) feature representations by end-to-end learning, the 'usual' ComParE and BoAW features, and deep unsupervised representation learning using the AUDEEP toolkit for the first time in the challenge series. © 2018 International Speech Communication Association. All rights reserved.

关键词： Heart

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LOCALIZE HEAVILY OCCLUDED HUMAN FACES VIA DEEP SEGMENTATION

LOCALIZE HEAVILY OCCLUDED HUMAN FACES VIA DEEP SEGMENTATION

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IEEE International Conference on Image Processing

作者： Kaihao Zhang Yongzhen Huang Ran He Hong Wu Liang Wang Statistical Machine Intelligence & LEarning School of Computer Science & Engineering University of Electronic Science and Technology of China National Laboratory of Pattern Recognition CAS Center for Excellence in Brain Science and Intelligence Technology Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781467399623

Localizing heavily occluded human faces is a challenging problem in facial detection. Previous methods mainly employ sliding windows by determining whether windows include human faces. In this paper, we provide a novel segmentation-based perspective for heavily occluded face localization with deep convolutional neural networks (CNN). Our model takes an image as input without complicated pre-processing. After several convolutional layers, fully-connected layers and a softmax classifier, we can predict the labels of all pixels in an image, which is the key to localize heavily occluded human faces. Finally, we search a minimal rectangle to localize the human face. Our detector needs neither complex pre-processing nor the time-consuming sliding window. Besides, we use a single model to localize faces to further alleviate computational complexity. Experimental results show that our proposed method is a very effective way to localize heavily occluded human face.

关键词： Facial localization Heavily occluded faces Deep convolutional neural networks Human occluding Imagery (Psychotherapy) images Face sliding windows FENSTER windows windows pixel Graphics input

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Keyword spotting for self-training of BLSTM NN based handwriting recognition systems

Keyword spotting for self-training of BLSTM NN based handwri...

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作者： Frinken, Volkmar Fischer, Andreas Baumgartner, Markus Bunke, Horst Computer Vision Center Autonomous University of Barcelona Edifici O 08193 Bellaterra Barcelona Spain Centre for Pattern Recognition and Machine Intelligence Concordia University 1455 de Maisonneuve Blvd West Montreal QC H3G 1M8 Canada Institute of Computer Science and Applied Mathematics University of Bern Neubrückstrasse 10 CH-3012 Bern Switzerland

The automatic transcription of unconstrained continuous handwritten text requires well trained recognition systems. The semi-supervised paradigm introduces the concept of not only using labeled data but also unlabeled data in the learning process. Unlabeled data can be gathered at little or not cost. Hence it has the potential to reduce the need for labeling training data, a tedious and costly process. Given a weak initial recognizer trained on labeled data, self-training can be used to recognize unlabeled data and add words that were recognized with high confidence to the training set for re-training. This process is not trivial and requires great care as far as selecting the elements that are to be added to the training set is concerned. In this paper, we propose to use a bidirectional long short-term memory neural network handwritten recognition system for keyword spotting in order to select new elements. A set of experiments shows the high potential of self-training for bootstrapping handwriting recognition systems, both for modern and historical handwritings, and demonstrate the benefits of using keyword spotting over previously published self-training schemes. © 2013 Elsevier Ltd. All rights reserved.

关键词： Character recognition

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Verification of Hierarchical Classifier Results for Handwritten Arabic Word Spotting

Verification of Hierarchical Classifier Results for Handwrit...

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International Conference on Document Analysis and recognition

作者： Muna Khayyat Louisa Lam Ching Y. Suen Center for Pattern Recognition and Machine Intelligence Concordia University Montreal QUE Canada

ISBN: (纸本)9781479901937

Large amounts of handwritten documents have been digitized, and the need to search and index these documents is increasing to make them more accessible. Different word spotting systems have been proposed to search for words for this purpose. Since the precision of the word spotting system is crucial, verifying the results of a word spotting system is becoming an effective approach to improve the system performance. In this paper, we propose two verification models for Arabic word spotting systems. Both models make use of a holistic classifier. The first model is based on matching the results of the word spotting system with those of the holistic classifier, while the other model derives a new score evaluation based on the two results. Verifying a word spotting system using these models can significantly improve its performance, since the precision rate increased from 74% to 77.7% and 84.4% respectively with the Word Matching and Score Evaluation models of verification, at 50% recall.

关键词： Feature extraction Databases Mathematical model Handwriting recognition Hidden Markov models Testing Support vector machines

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A fast matching algorithm for graph-based handwriting recognition

A fast matching algorithm for graph-based handwriting recogn...

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9th IAPR-TC-15 International Workshop on Graph-Based Representations in pattern recognition, GbRPR 2013

作者： Fischer, Andreas Suen, Ching Y. Frinken, Volkmar Riesen, Kaspar Bunke, Horst Centre for Pattern Recognition and Machine Intelligence Concordia University 1455 de Maisonneuve Blvd West Montreal QC H3G 1M8 Canada Computer Vision Center Dept. of Computer Science Universitat Autònoma de Barcelona 08193 Bellaterra Spain Institute for Informations Systems University of Applied Sciences and Arts Northwestern Switzerland Riggenbachstrasse 16 4600 Olten Switzerland Institute of Computer Science and Applied Mathematics University of Bern Neubrückstrasse 10 3012 Bern Switzerland

ISBN: (纸本)9783642382208

The recognition of unconstrained handwriting images is usually based on vectorial representation and statistical classification. Despite their high representational power, graphs are rarely used in this field due to a lack of efficient graph-based recognition methods. Recently, graph similarity features have been proposed to bridge the gap between structural representation and statistical classification by means of vector space embedding. This approach has shown a high performance in terms of accuracy but had shortcomings in terms of computational speed. The time complexity of the Hungarian algorithm that is used to approximate the edit distance between two handwriting graphs is demanding for a real-world scenario. In this paper, we propose a faster graph matching algorithm which is derived from the Hausdorff distance. On the historical Parzival database it is demonstrated that the proposed method achieves a speedup factor of 12.9 without significant loss in recognition accuracy. © 2013 Springer-Verlag.

关键词： Vector spaces

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Arabic handwritten text line extraction by applying an adaptive mask to morphological dilation

Arabic handwritten text line extraction by applying an adapt...

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

作者： Khayyat, Muna Lam, Louisa Suen, Ching Y. Yin, Fei Liu, Cheng-Lin Concordia University Center for Pattern Recognition and Machine Intelligence Montreal QC H3G 1M8 Canada Institute of Automation of Chinese Academy of Science National Laboratory of Pattern Recognition Beijing 100190 China

ISBN: (纸本)9780769546612

This paper presents a robust method for handwritten text line extraction. We use morphological dilation with a dynamic adaptive mask for line extraction. Line separation occurs because of the repulsion and attraction between connected components. The characteristics of the Arabic script are considered to ensure a high performance of the algorithm. Our method is evaluated on the CENPARMI Arabic handwritten documents database which contains multi-skewed and touching lines. With a matching score of 0.95, our method achieved precision and recall rates of 96:3% and 96:7% respectively, which demonstrate the effectiveness of our approach. © 2012 IEEE.

关键词： Extraction

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Arabic Handwritten Text Line Extraction by Applying an Adaptive Mask to Morphological Dilation

Arabic Handwritten Text Line Extraction by Applying an Adapt...

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IAPR International Workshop on Document Analysis Systems, DAS

作者： Muna Khayyat Louisa Lam Ching Y. Suen Fei Yin Cheng-Lin Liu Center for Pattern Recognition and Machine Intelligence Concordia University Montreal QUE Canada Institute of Automation National Laboratory of Pattern Recognition Chinese Academy of Sciences Beijing China

关键词： Heuristic algorithms Databases Clustering algorithms Text analysis Layout Shape

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Discovering Legible Chinese Typefaces for Reading Digital Documents

Discovering Legible Chinese Typefaces for Reading Digital Do...

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International Conference on Document Analysis and recognition

作者： Bing Zhang Ying Li Ching Y. Suen Xuemin Zhang CENPARMI (Center for Pattern Recognition and Machine Intelligence) Computer Science and Software Engineering Department Concordia University Montreal Canada School of Psychology Beijing Normal University Beijing China

ISBN: (纸本)9781457713507

More and more fonts have sprung up in recent years in digital publishing industry and reading devices. In this paper, we focus on methods of evaluating digital Chinese fonts and their typeface characteristics to seek a good way to enhance the character recognition rate. To accomplish this, we combined psychological analysis methods with statistical analysis. It involved an extensive survey of distinctive features of eighteen popular digital typefaces. Survey results were tabulated and analyzed statistically. Then another objective experiment was conducted using the best six fonts derived from the survey results. These experimental results reveal an effective way of choosing legible digital fonts most suitable for comfortable reading of books, magazines, newspapers, and for display of texts on cell-phones, e-books, and digital libraries, and finding out the features for improving character legibility of different Chinese typefaces. The relationships among legibility, eye-strain, and myopia, will be discussed.

关键词： Character recognition Educational institutions Periodic structures Text analysis Accuracy Electronic publishing Statistical analysis

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Word Spotting in Gray Scale Handwritten Pashto Documents

Word Spotting in Gray Scale Handwritten Pashto Documents

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International Workshop on Frontiers in Handwriting recognition

作者： Muhammad Ismail Shah Ching Y. Suen CENPARMI (Center of Pattern Recognition and Machine Intelligence) Department of Computer Science and Software Engineering Concordia University Montreal QUE Canada

In this paper, we present an approach for word spotting in Gray-scale Pashto Documents, written in modified Arabic scripts. Various profile and transitional features are extracted from gray-scale word images. The gray-scale feature vectors are then converted into binary feature vectors by replacing each value within the gray-scale feature vectors with its binary equivalents. In this way, we have enabled the alignment of the gray-scale feature vectors via a faster binary pattern matching algorithm, i.e., Correlation Similarity Measure (CORR). The approach has effectively handled the handwriting variations of 200 different writers. The average precision rate achieved is 94.75 % for an average recall of 60.25%. The time taken for matching every set of two word images is 1.43 ms.

关键词： Feature extraction Gray-scale Image matching Correlation Heuristic algorithms Databases Hidden Markov models

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