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1st International Conference on Pervasive Computing Advances and Applications, PerCAA 2019

作者： Vinay, A. Acharya, Harshith R. Singh, Harshita Satyanarayana, Vibha Murthy, K.N.B. Natarajan, S. Deptartment of Computer Science PES University Banashankari 3rd Stage Bangalore Karnataka560085 India Center for Pattern Recognition and Machine Intelligence PES University Banashankari 3rd Stage Bangalore Karnataka560085 India

In today’s diversified world, with every person having their own idiosyncratic identity, the field of face detection and recognition has received significant scope. Since birth, humans develop and harvest similar facial features, making their recognition a challenging problem. Thus, the development of faster, more accurate and robust algorithms is of prime importance. The most critical requirements in developing a reliable face recognition algorithm is a large database of facial images and a well-developed systematic procedure to evaluate the system. In this paper, we bring out an approach to implement quick facial recognition using the concept of vocabulary trees in accordance with ORB, SIFT and SURF detectors and compare their performance. The model is trained using the Grimace, Face95 and ORL databases. The experiments are conducted by employing various state-of-the-art clustering algorithms and the results are compared for accuracy. The results and comparisons obtained on the databases show that the ORB detector clustered using Spectral clustering algorithm to construct the Vocabulary Tree outperforms the other techniques. © 2019 The Authors. Published by Elsevier Ltd.

关键词： Clustering algorithms

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Deep learning driven interpretable and informed decision making model for brain tumour prediction using explainable AI

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Scientific reports 2025年第1期15卷 19223页

作者： Khan Muhammad Adnan Taher M Ghazal Muhammad Saleem Muhammad Sajid Farooq Chan Yeob Yeun Munir Ahmad Sang-Woong Lee Pattern Recognition and Machine Learning Lab Faculty of Artificial Intelligence and Software Gachon University Seongnam-si 13557 Republic of Korea. Department of Networks and Cybersecurity Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19111 Jordan. Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab 140401 India. Department of Cyber Security NASTP Institute of Information Technology Lahore 58810 Pakistan. Center for Secure Cyber-Physical Systems (C2PS) Computer Science Department Khalifa University Abu Dhabi United Arab Emirates. chan.yeun@ku.ac.ae. University College Korea University Seoul 02841 Republic of Korea. Pattern Recognition and Machine Learning Lab Faculty of Artificial Intelligence and Software Gachon University Seongnam-si 13557 Republic of Korea. slee@gachon.ac.kr.

Brain Tumours are highly complex, particularly when it comes to their initial and accurate diagnosis, as this determines patient prognosis. Conventional methods rely on MRI and CT scans and employ generic machine learning techniques, which are heavily dependent on feature extraction and require human intervention. These methods may fail in complex cases and do not produce human-interpretable results, making it difficult for clinicians to trust the model's predictions. Such limitations prolong the diagnostic process and can negatively impact the quality of treatment. The advent of deep learning has made it a powerful tool for complex image analysis tasks, such as detecting brain Tumours, by learning advanced patterns from images. However, deep learning models are often considered "black box" systems, where the reasoning behind predictions remains unclear. To address this issue, the present study applies Explainable AI (XAI) alongside deep learning for accurate and interpretable brain Tumour prediction. XAI enhances model interpretability by identifying key features such as Tumour size, location, and texture, which are crucial for clinicians. This helps build their confidence in the model and enables them to make better-informed decisions. In this research, a deep learning model integrated with XAI is proposed to develop an interpretable framework for brain Tumour prediction. The model is trained on an extensive dataset comprising imaging and clinical data and demonstrates high AUC while leveraging XAI for model explainability and feature selection. The study findings indicate that this approach improves predictive performance, achieving an accuracy of 92.98% and a miss rate of 7.02%. Additionally, interpretability tools such as LIME and Grad-CAM provide clinicians with a clearer understanding of the decision-making process, supporting diagnosis and treatment. This model represents a significant advancement in brain Tumour prediction, with the potential to enhance pat

关键词： Brain tumour CT scans MRI XAI

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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

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.

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

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On Detectors and Descriptors based Techniques for Face recognition

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Procedia Computer Science 2018年 132卷 908-917页

作者： Vinay A Nishant Aklecha Meghana K.N. Balasubramanya Murthy S Natarajan 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.

关键词： Face Recognision descriptors detectors features HFT Harris-Stephen Minimum Eigen SURF feature extraction LGHD PCEHD EHD RANSAC

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OLSR+: A new routing method based on fuzzy logic in flying ad-hoc networks (FANETs)

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Vehicular Communications 2022年 36卷

作者： Rahmani, Amir Masoud Ali, Saqib Yousefpoor, Efat Yousefpoor, Mohammad Sadegh Javaheri, Danial Lalbakhsh, Pooia Hassan Ahmed, Omed Hosseinzadeh, Mehdi Lee, Sang-Woong Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Information Systems College of Economics and Political Science Sultan Qaboos University Al Khoudh Muscat Oman Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Computer Engineering Chosun University Gwangju 61452 South Korea Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University Clayton 3800 VIC Australia Department of Information Technology University of Human Development Sulaymaniyah Iraq Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam 13120 South Korea

Flying ad-hoc networks (FANETs) have many applications in military, industrial and agricultural areas. Due to specific features of FANETs, such as high-speed nodes, low density of nodes in the network, and rapid changes in the topology, most routing protocols designed for mobile ad hoc networks (MANETs) or vehicular ad hoc networks (VANETs) are not compatible with FANETs. In this paper, we propose a fuzzy logic-based routing approach called OLSR+ for FANETs. In this scheme, we seek to improve the optimized link state routing protocol (OLSR) so that it can efficiently be used in FANETs. OLSR+ includes four main phases: 1) Discovering neighboring nodes. In this phase, we propose a new and efficient technique for estimating the lifetime of the link between two unmanned ariel vehicles (UAVs) based on the link quality, distance, relative velocity, and movement direction. 2) Selecting multipoint relays (MPRs). In this phase, we present a fuzzy mechanism for selecting a set of MPR nodes. According to this mechanism, when a node has higher residual energy, higher link lifetime, and more neighborhood degree compared to others, it achieves more fitness to be selected as MPR. 3) Discovering the network topology. In this phase, we modify the format of the topology control (TC) message and add two fields, including route energy and route lifetime to this message. 4) Calculating the routing table. In OLSR+, we consider two parameters, including route energy and route lifetime, for establishing stable paths. Finally, we simulate OLSR+ using NS3 and compare its performance with two methods, namely greedy optimized link state routing (G-OLSR) and optimized link state routing (OLSR). The simulation results show that OLSR+ successfully reduces delay compared to G-OLSR and OLSR. In addition, it has higher packet delivery rate and throughput than others. Also, it improves energy consumption in the network. However, OLSR+ has more routing overhead than G-OLSR. © 2022 Elsevier Inc.

关键词： Artificial intelligence (AI) Flying ad hoc networks (FANETs) Fuzzy logic Routing Unmanned ariel vehicles (UAVs)

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Unconstrained Face recognition using ASURF and Cloud-Forest Classifier optimized with VLAD

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Procedia Computer Science 2018年 143卷 570-578页

作者： Vinay A Aviral Joshi Hardik Mahipal Surana Harsh Garg K N Balasubramanya Murthy S Natarajan Center for Pattern Recognition and Machine Intelligence PES University Bangalore 560085 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.

关键词： Face recognition Haar Cascade Bilateral Filter ASURF Bag of Words VLAD Cloud Forest Random Forest

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Aggregation of Deep Local Features using VLAD and Classification using R 2 Forest

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Procedia Computer Science 2018年 143卷 998-1006页

作者： Vinay A Harsh Garg Ankit Anand Rajat Nigam Abhijay Gupta K N Balasubramanya Murthy S Natarajan Center for Pattern Recognition and Machine Intelligence PES University Bangalore 560085 India

The paper proposes an efficient and accurate model for face recognition using an attentive local feature descriptor extracted from Convolutional Neural Network referred to as DEep Local Feature (DELF). The algorithm mentioned formerly is used for extracting descriptors from the images using a fully convolutional network which are trained with weak supervision and using image level classes, neglecting the usage of patch and object level annotations. The physical characteristics such as colour, texture, etc are represented in the form of 40 dimensional vectors using DELF. Further, such descriptors are quantized to represent them into the compact form using Vector of Locally Aggregated Descriptors and Fisher kernels. Subsequently, such vectors are used for multi-class image classification using ensemble learning methods including Rotation Forest and Random Forests. Comparative study between both the classifiers and feature aggregation methods are performed and tabulated in the paper.

关键词： DELF Rotation Forest Fisher Kernel VLAD Face recognition

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Vocabulary Trees with OSS Detectors

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Procedia Computer Science 2019年 152卷 282-290页

作者： A Vinay Harshith R Acharya Harshita Singh Vibha Satyanarayana K N B Murthy S Natarajan Deptartment of Computer Science PES University Banashankari 3rd Stage Bangalore 560085 Karnataka India Center for Pattern Recognition and Machine Intelligence PES University Banashankari 3rd Stage Bangalore 560085 Karnataka India

关键词： Facial recognition Detectors Clustering Vocabulary Tree Image Databases

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Sequence generation: From both sides to the middle

arXiv

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arXiv 2019年

作者： Zhou, Long Zhang, Jiajun Zong, Chengqing Yu, Heng University of Chinese Academy of Sciences Beijing China National Laboratory of Pattern Recognition CASIA Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai China Machine Intelligence Technology Lab Alibaba Group

The encoder-decoder framework has achieved promising process for many sequence generation tasks, such as neural machine translation and text summarization. Such a framework usually generates a sequence token by token from left to right, hence (1) this autoregressive decoding procedure is time-consuming when the output sentence becomes longer, and (2) it lacks the guidance of future context which is crucial to avoid under-translation. To alleviate these issues, we propose a synchronous bidirectional sequence generation (SBSG) model which predicts its outputs from both sides to the middle simultaneously. In the SBSG model, we enable the left-to-right (L2R) and right-to-left (R2L) generation to help and interact with each other by leveraging interactive bidirectional attention network. Experiments on neural machine translation (En-De, Ch-En, and En-Ro) and text summarization tasks show that the proposed model significantly speeds up decoding while improving the generation quality compared to the autoregressive Transformer. Copyright © 2019, The Authors. All rights reserved.

关键词： Neural machine translation

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