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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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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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Face recognition using interest points and ensemble of classifiers 4

Face recognition using interest points and ensemble of class...

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4th IEEE International Conference on Recent Advances in Information Technology, RAIT 2018

作者： Vinay, A. Sampat, Pratik Rajesh Belavadi, Sagar V. Pratik, R. Rao, B. S. Nikitha Ragesh, Rahul Murthy, K. N. Balasubramanya Natarajan, S. Centre for Pattern Recognition and Machine Intelligence PES University Bangalore Karnataka India

ISBN: (纸本)9781538630396

In human beings, it is the responsibility of the temporal lobe of the brain for recognition of faces. Certain features of the face trigger the neurons of the temporal lobe which are then stored. These eventually lead to the identification of the face. In machine learning system, a huge dataset of images is used as a reference by the face recognition algorithms. The most prominent features are extracted from what is referred to as landmarks on the face and are analyzed based on their relative position amongst other features. The main challenge in computer vision lies in the identification and extraction of these features which in turn assist in face recognition owing to the exponentially large degree of variability that can be seen in appearances. Speed and accuracy are some of the most prominent challenges for the same. We explore ELM critically and study its performance. Owing to its extremely fast learning, it finds potential in real-time applications. The goal of this paper is to evaluate the performance of the extreme learning machine algorithm for facial recognition when used as a standalone model as well as when it is used with an ensemble learner. We then conclude with a voting classifier which is responsible for the final predictions made. Various combinations of ensembles are explored to come up with the best performing model. © 2018 IEEE.

关键词： 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

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.

关键词： Facial recognition Detectors Clustering Vocabulary Tree Image Databases

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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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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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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 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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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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