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ICDAR2009 - 10th International Conference on Document Analysis and recognition

作者： Deng, Huiqun Stathopoulos, George Suen, Ching Y. Center for Pattern Recognition and Machine Intelligence Concordia University Canada

ISBN: (纸本)9780769537252

It is known that convolutional neural networks (CNNs) are efficient for optical character recognition (OCR) and many other visual classification tasks. This paper applies error-correcting output coding (ECOC) to the CNN for segmentation-free OCR such that: 1) the CNN target outputs are designed according to code words of length N;2) the minimum Hamming distance of the code words is designed to be as large as possible given N. ECOC provides the CNN with the ability to reject or correct output errors to reduce character insertions and substitutions in the recognized text. Also, using code words instead of letter images as the CNN target outputs makes it possible to construct an OCR for a new language without designing the letter images as the target outputs. Experiments on the recognition of English letters, 10 digits, and some special characters show the effectiveness of ECOC in reducing insertions and substitutions. © 2009 IEEE.

关键词： Optical character recognition

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Isolated handwritten Farsi numerals recognition using sparse and over-complete representations

Isolated handwritten Farsi numerals recognition using sparse...

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ICDAR2009 - 10th International Conference on Document Analysis and recognition

作者： Pan, W.M. Bui, T.D. Suen, C.Y. Center for Pattern Recognition and Machine Intelligence Concordia University Canada

ISBN: (纸本)9780769537252

A new isolated handwritten Farsi numeral recognition algorithm is proposed in this paper, which exploits the sparse and over-complete structure from the handwritten Farsi numeral data. In this research, the sparse structure is represented as an over-complete dictionary, which is learned by the K-SVD algorithm. These atoms in this dictionary are adopted to initialize the first layer of the Convolutional Neural Network (CNN), the latter is then trained to do the classification task. Data distortion techniques are also applied to promote the generalization capability of the trained classifier. Experiments have shown that good results have been achieved in CENPARMI handwritten Farsi numeral database. © 2009 IEEE.

关键词： Neural networks

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Deep Learning on Binary patterns for Face recognition

Deep Learning on Binary Patterns for Face Recognition

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2018 International Conference on Computational intelligence and Data Science, ICCIDS 2018

作者： Vinay, A. Gupta, Abhijay Bharadwaj, Aprameya Srinivasan, Arvind Murthy, K.N. Balasubramanya Natarajan, S. Center for Pattern Recognition and Machine Intelligence PES University Bengaluru India

In this paper an efficient and robust method for real-time face recognition is proposed. As a part of pre-processing to remove noise and unwanted features, a filter is applied to the images of standard datasets. Subsequently binary patterns are extracted from these images which are further fed into multilayer perceptron to classify images. The proposed method is tested on four benchmark datasets namely FACES94, FACES95, FACES96 and Grimace which pose challenges in illumination, pose, expression, head scale and rotation. This method delivers accuracies in the neighbourhood of 91% when tested on these datasets. Our methodology was further extended to embedded systems like Raspberry Pi 3 to give it more real-time and practical scenario. This test gave promising results and proved the model to be efficient and useful in day to day scenarios for face recognition. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Neural networks

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Enhancement of Degraded CCTV Footage for Forensic Analysis 3rd

Enhancement of Degraded CCTV Footage for Forensic Analysis

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3rd International Conference on Innovative Computing and Communication, ICICC 2020

作者： Vinay, A. Lokesh, Aditya Kamath, Vinayaka R. Murty, K.N.B. Natarajan, S. Center for Pattern Recognition and Machine Intelligence PES University Bengaluru India

ISBN: (纸本)9789811551123

Forensic analysis has proved to be one of the most utilitarian tool in investigating crime. Forensic analysis provides evidence/basic information of the said crime through analysis of physical evidence. In this paper, we present a scintillating technique to enhance the image quality of CCTV video to assist in the investigation of criminal cases. This paper, address the issue of lack of a budget surplus for a high-quality image/video capturing device. The problem of enhancing images is addressed by pure image processing methods and machine learning techniques. The paper analyzes both techniques and further concludes that the machine learning approach produces a more efficient result. The applications of this technique can range from simple image filtering/beautification to forensic image processing. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Crime

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Sparse Locally Adaptive Regression Kernel for Face Verification

Sparse Locally Adaptive Regression Kernel for Face Verificat...

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2018 International Conference on Computational intelligence and Data Science, ICCIDS 2018

作者： Vinay, A. Kamath, Vinayaka R. Varun, M. Murthy, K.N. Balasubramanya Natarajan, S. Center for Pattern Recognition and Machine Intelligence PES University Bengaluru India

The paper presents several thresholds obtained by heuristic approach for face verification using Locally Adaptive Regression Kernel (LARK) descriptors for euclidean, cosine and chebyshev distance metrics. The absence of a threshold for several distance metrics possess several setbacks such as increased computational complexity and escalated runtime. The proposed method has significantly higher influence when the verification process is computationally intensive. The proposed approach requires minimal training and face verification is accomplished based on the threshold value obtained during training. The whole process is modest and nearly all of the time spent is solely on quantifying any of the distance metrics between the LARKs obtained from the two faces for verification. LARK descriptors compute a measure of resemblance on the basis of "signal-induced distance" between a pixel and its nearby pixels. We assess the interspace between the LARKs from these faces and analogize the resemblance from the threshold resulting in a binary decision. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Pixels

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On Detectors and Descriptors based Techniques for Face recognition

On Detectors and Descriptors based Techniques for Face Recog...

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2018 International Conference on Computational intelligence and Data Science, ICCIDS 2018

作者： Vinay, A. Aklecha, Nishant Meghana Murthy, K.N. Balasubramanya Natarajan, S. Center for Pattern Recognition and Machine Intelligence PES University Bengaluru India

Out of all forms of biometrics, Face recognition (FR) emerges as the most incredible one. Apart from offering revolutionary applications for business and law-enforcement purposes, it has also opened numerous research avenues in various domains like security, surveillance and social network. One of the many factors critical to having an efficient face recognition system is having at hand, a suitable combination of feature descriptor and feature detector. A feature detector makes use of methods that make local decisions regarding the presence/absence of image features of a given type. A feature descriptor, on the other hand, simplifies the image by extracting useful information and disposing irrelevant information. Our research discusses the goodness of various feature descriptor-detector combination. We do this by simply carrying out the process of feature matching using various combinations of detectors and descriptors. This experiment includes incorporation of dimensionality reduction on the images using Hypercomplex Fourier Transform (HFT) and RANSAC for noise reduction. Out of the diverse options available, we chose to test LGHD, PCEHD and EHD for feature descriptors;for feature detectors, we chose the ones that make use of popular algorithms like Harris-Stephen Algorithm, Minimum Eigen Value and SURF. A series of strict and thorough experiments on popularly available datasets - Faces94 and Grimace led us to an astonishing observation - an accuracy of 90.67% for the former and 71.3% for the latter for Minimum Eigen Value paired with LGHD! This in comparison to those for all other combinations is a lot superior. We thereby conclusively state that feature detector using Minimum Eigen Value algorithm paired with feature descriptor - LGHD outplays other combinations making this combination the best choice for Face recognition. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Feature extraction

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Optimal KAZE and AKAZE Features for Facial Similarity Matching 1

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Proceedings of the 7th International Conference on Advances in Computing and Data Sciences, ICACDS 2023

作者： Vinay, A. Vasu, Kishan Athirala Lodha, Pranav Yogi Natarajan, S. Sudarshan, T.S.B. Center for Pattern Recognition and Machine Intelligence PES University Bengaluru560085 India

ISBN: (数字)9783031379406

ISBN: (纸本)9783031379390

Face recognition is one of the premier disciplines in the vast field of computer vision and image analysis. A popular method is the Gaussian scale space analysis which limits the performance by smoothing both the noise and natural boundaries in the same proportion. In order to prevent the loss of natural boundaries to smoothing we tap into nonlinear scale space techniques such as KAZE and Accelerated KAZE. KAZE is a multistage 2-D feature detection and description algorithm. It makes use of AOS schemes to develop the nonlinear scale space for analysis. Though the results are satisfactory, it is computationally intense as they solve a humongous module of linear equations. To ascertain the mentioned limitation of KAZE we make use of Accelerated-KAZE, which uses pyramidal structure with Fast Explicit Diffusion incorporated in it, thus minimizing the computation in the step of feature detection of nonlinear scale space. Also, with the use of M-LDB (Modified-Local Difference Binary) descriptor the problem of rotation is solved. Usage of RANSAC after processing in two methods had some disadvantages. It gave bad results when the inliers ratio in the dataset is low. Thus, Optimal RANSAC is employed which works well even when the inliers ratio is as low as 5%. The proposed methods are tested on many standard datasets and various performance parameters. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Face recognition

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Four directional adjacency graphs (FDAG) and their application in locating fields in forms 3

Four directional adjacency graphs (FDAG) and their applicati...

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3rd International Conference on Document Analysis and recognition, ICDAR 1995

作者： Yuan, Jianxing Tang, Y.Y. Suen, C.Y. Center for Pattern Recognition and Machine Intelligence Concordia University MontrealH3T1E8 Canada

ISBN: (纸本)0818671289

A new non-hierarchical spatial data structure named four directional adjacency graphs (FDAG) is proposed. In the FDAG vertical and horizontal neighborhood relationship between rectangles is well represented so that structural information can be easily extracted. An application for structural analysis of forms is given, where experiments are conducted with positive results. © 1995 IEEE.

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Sparse Locally Adaptive Regression Kernel For Face Verification

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Procedia Computer Science 2018年 132卷 890-899页

作者： Vinay A Vinayaka R Kamath Varun M K N Balasubramanya Murthy S Natarajan Center for Pattern Recognition and Machine Intelligence PES University Bengaluru India

The paper presents several thresholds obtained by heuristic approach for face verification using Locally Adaptive Regression Kernel (LARK) descriptors for euclidean, cosine and chebyshev distance metrics. The absence of a threshold for several distance metrics possess several setbacks such as increased computational complexity and escalated runtime. The proposed method has significantly higher influence when the verification process is computationally intensive. The proposed approach requires minimal training and face verification is accomplished based on the threshold value obtained during training. The whole process is modest and nearly all of the time spent is solely on quantifying any of the distance metrics between the LARKs obtained from the two faces for verification. LARK descriptors compute a measure of resemblance on the basis of “signal-induced distance” between a pixel and its nearby pixels. We assess the interspace between the LARKs from these faces and analogize the resemblance from the threshold resulting in a binary decision.

关键词： LARK Face Verification SVD Filtering Threshold

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Deep Learning on Binary patterns for Face recognition

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Procedia Computer Science 2018年 132卷 76-83页

作者： A Vinay Abhijay Gupta Aprameya Bharadwaj Arvind Srinivasan K N Balasubramanya Murthy S Natarajan Center for Pattern Recognition and Machine Intelligence PES University Bengaluru India

关键词： Local Binary Patters Gaussian Filter MLP Classifier Artificial Neural Network Bag of Words Raspberry Pi 3

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