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Optimal KAZE and AKAZE Features for Facial Similarity Matching 1

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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Enhancement of Degraded CCTV Footage for Forensic Analysis 3rd

Enhancement of Degraded CCTV Footage for Forensic Analysis

引用

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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Unconstrained face recognition using asurf and cloud-forest classifier optimized with VLAD

arXiv

引用

arXiv 2021年

作者： Vinay, A. A., Joshi H.M., Surana H., Garg K.N.B., Murthy S., Natarajan Center for Pattern Recognition and Machine Intelligence PES University Bangalore560085 India

The paper posits a computationally-efficient algorithm for multi-class facial image classification in which images are constrained with translation, rotation, scale, color, illumination and affine distortion. The proposed method is divided into five main building blocks including Haar-Cascade for face detection, Bilateral Filter for image preprocessing to remove unwanted noise, Affine Speeded-Up Robust Features (ASURF) for keypoint detection and description, Vector of Locally Aggregated Descriptors (VLAD) for feature quantization and Cloud Forest for image classification. The proposed method aims at improving the accuracy and the time taken for face recognition systems. The usage of the Cloud Forest algorithm as a classifier on three benchmark datasets, namely the FACES95, FACES96 and ORL facial datasets, showed promising results. The proposed methodology using Cloud Forest algorithm successfully improves the recognition model by 2-12% when differentiated against other ensemble techniques like the Random Forest classifier depending upon the dataset used. © 2021, CC BY.

关键词： Face recognition

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Convolutional Neural Network Based Intelligent Handwritten Document recognition

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Computers, Materials & Continua 2022年第3期70卷 4563-4581页

作者： Sagheer Abbas Yousef Alhwaiti Areej Fatima Muhammad A.Khan Muhammad Adnan Khan Taher M.Ghazal Asma Kanwal Munir Ahmad Nouh Sabri Elmitwally School of Computer Science National College of Business administration and EconomicsLahore54000Pakistan College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)Bangi 43600SelangorMalaysia School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE Department of Computer Science GC UniversityLahorePakistan Department of Computer Science Faculty of Computers and Artificial IntelligenceCairo University12613Egypt

This paper presents a handwritten document recognition system based on the convolutional neural network *** today’s world,handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired *** technology is also helpful for the automatic data entry *** the proposed systemprepared a dataset of English language handwritten character *** proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten *** this research,multiple experiments get very worthy recognition *** proposed systemwill first performimage pre-processing stages to prepare data for training using a convolutional neural *** this processing,the input document is segmented using line,word and character *** proposed system get the accuracy during the character segmentation up to 86%.Then these segmented characters are sent to a convolutional neural network for their *** recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given *** proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%,and for validation that accuracy slightly decreases with 90.42%.

关键词： Convolutional neural network segmentation skew cursive characters recognition

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DisenE: Disentangling knowledge graph embeddings

arXiv

引用

arXiv 2020年

作者： Kou, Xiaoyu Lin, Yankai Li, Yuntao Xu, Jiahao Li, Peng Zhou, Jie Zhang, Yan Department of Machine Intelligence Peking University Beijing China Pattern Recognition Center WeChat AI Tencent Inc. China

Knowledge graph embedding (KGE), aiming to embed entities and relations into low-dimensional vectors, has attracted wide attention recently. However, the existing research is mainly based on the black-box neural models, which makes it difficult to interpret the learned representation. In this paper, we introduce DisenE, an end-to-end framework to learn disentangled knowledge graph embeddings. Specially, we introduce an attention-based mechanism that enables the model to explicitly focus on relevant components of entity embeddings according to a given relation. Furthermore, we introduce two novel regularizers to encourage each component of the entity representation to independently reflect an isolated semantic aspect. Experimental results demonstrate that our proposed DisenE investigates a perspective to address the interpretability of KGE and is proved to be an effective way to improve the performance of link prediction tasks. The code and datasets are released on https://***/KXY-PUBLIC/DisenE. Copyright © 2020, The Authors. All rights reserved.

关键词： Knowledge graph

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Does color modalities affect handwriting recognition? An empirical study on Persian handwritings using convolutional neural networks

arXiv

引用

arXiv 2023年

作者： Zohrevand, Abbas Imani, Zahra Sadri, Javad Suen, Ching Y. Dept. of Computer Engineering Kosar University of Bojnord Bojnord Iran Camp in Labs Foundation 1 Westmount Square Bureau 1001 WestmountQCH3Z 2P9 Canada Dept. of Computer Science and Software Engineering Faculty of Engineering and Computer Science Concordia University MontrealQC Canada Center for Pattern Recognition and Machine Intelligence Concordia University MontrealH3G 1M8 Canada

Most of the methods on handwritten recognition in the literature are focused and evaluated on Black and White (BW) image databases. In this paper we try to answer a fundamental question in document recognition. Using Convolutional Neural Networks (CNNs), as eye simulator, we investigate to see whether color modalities of handwritten digits and words affect their recognition accuracy or speed? To the best of our knowledge, so far this question has not been answered due to the lack of handwritten databases that have all three color modalities of handwritings. To answer this question, we selected 13,330 isolated digits and 62,500 words from a novel Persian handwritten database, which have three different color modalities and are unique in term of size and variety. Our selected datasets are divided into training, validation, and testing sets. Afterwards, similar conventional CNN models are trained with the training samples. While the experimental results on the testing set show that CNN on the BW digit and word images has a higher performance compared to the other two color modalities, in general there are no significant differences for network accuracy in different color modalities. Also, comparisons of training times in three color modalities show that recognition of handwritten digits and words in BW images using CNN is much more efficient. © 2023, CC BY.

关键词： Color

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Effective Utilization of Whitening for Person Identification

SSRN

引用

SSRN 2019年

作者： Vinay, A. Kailasam, Siddharth Bharadwaj, Aprameya Balasubramanya, K.N. Natarajan, Murthy S. Center for Pattern Recognition and Machine Intelligence PES University Bangalore India

The growth of importance of Person Identification(PI) is so drastic these days, since it helps support the growth of many other fields like surveillance, robotics etc. Computer Vision Enthusiasts are working on making this robust to various factors such as noise, scale, pose, intensity, illumination, translation, expression and many more. Whitening is a pre-processing technique which makes an image robust to noise, intensity and illumination. There are many feature detectors such as FLANN matching algorithms, which use relatively lesser resources, but are not robust to one or more of the above mentioned problems. However, in general whitening is usually used with machine learning algorithms, which are already robust to these problems. This paper hence compares results obtained using SVD whitening and ZCA whitening to without pre-processing on four distinct pipelines. The first two pipelines use SIFT extracted keypoints and SURF extracted keypoints matched by KD-Tree FLANN matcher and classified using confidence probability matching. These pipelines as we know are not robust to the problems solved by whitening. They are implemented on ORL, grimace and faces 95 database. The third and fourth pipeline includes machine learning algorithms to classify the features extracted by Spatial Pyramid Matching and Earth Mover’s Distance on Dense SIFT keypoints. They were implemented on ORL, faces 95 and FEI database. Results show that SVD whitening and ZCA whitening increases the accuracy more when FLANN matcher is used than when machine learning algorithms are used to classify the images. In both cases, we encounter a scenario where the accuracy is slightly lesser than the accuracy obtained without whitening. However, we find that the decrease in accuracy is more when machine learning algorithms are used than when FLANN matchers are used. © 2019, The Authors. All rights reserved.

关键词： Pipelines

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Deep Learning on Binary patterns for Face recognition

Deep Learning on Binary Patterns for Face Recognition

引用

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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Sparse Locally Adaptive Regression Kernel for Face Verification

Sparse Locally Adaptive Regression Kernel for Face Verificat...

引用

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...

引用

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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