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S Sesha Vidhya

Bio: S Sesha Vidhya is an academic researcher from R.M.K. College of Engineering and Technology. The author has contributed to research in topics: Artificial intelligence & Face (sociological concept). The author has an hindex of 1, co-authored 5 publications receiving 12 citations.

Papers
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Journal ArticleDOI
TL;DR: A non-invasive passive flexible Ultra Wide Band (UWB) Myogram antenna sensor for the prediction of Sarcopenia through human muscle mass measurement and the proposed method of diagnosing Sarc Openia achieves an accuracy of 85% in fifty samples.

17 citations

Journal ArticleDOI
TL;DR: In this article, the authors explored, compared and compared various face identification algorithms such as Linear Discriminant Analysis (LDA), Local Binary Pattern Histogram (LBPH), Principal Component Analysis (PCA), Elastic Bunch Graph Matching (EBGM), and neural networks.

7 citations

Journal ArticleDOI
TL;DR: In this article, the authors developed a solution to help the task force using a face recognition based UAV to identify the criminals, missing people, civilians and for surveillance, which is a technology which involves the understanding of how the faces are detected and recognized.

4 citations

Proceedings ArticleDOI
25 Mar 2022
TL;DR: A way to assist the task force to track illicit, missing people and for surveillance employing a face recognition based drone employing Haar Cascade and Local Binary Pattern Histogram is proposed.
Abstract: Face recognition is a popular technique that uses image processing to identify people's faces. Face recognition is becoming momentous due to the growing populace, which necessitates high security and monitoring systems, as well as the need for self confirmation in the current era, fight in rustic regions, disaster relief, and so on. This study proposes a way to assist the task force to track illicit, missing people and for surveillance employing a face recognition based drone. Haar Cascade is a face detection algorithm that is used to make out faces in images and real-time videos. Local Binary Pattern Histogram (LBPH) is used to recognize a person's face. The drone yielded an accuracy of 98 percent in the identification of individuals there by finds significant applications.

2 citations

Journal ArticleDOI
TL;DR: This paper has proposed a technique which eliminates all the errors and illegal activities which happen at the initial stage of the examination process and has proved to provide a better security rather than the manual checkup process.

1 citations


Cited by
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Journal ArticleDOI
01 Apr 2022-Heliyon
TL;DR: In this paper , the authors used machine learning and deep learning for various antenna design applications, such as millimeter wave, body-centric, terahertz, satellite, unmanned aerial vehicle, global positioning system, and textiles.

19 citations

Journal ArticleDOI
TL;DR: In this article, a narrowband microstrip antenna resonating at 1.3 GHz is used as a microwave sensor for non-invasive measurement of blood glucose level (BGL) in diabetic patients.
Abstract: The diabetic patients use invasive technique to monitor their Blood Glucose Level (BGL) on a regular basis. This paper mainly presents a system that estimates BGL noninvasively using microwave techniques. A narrowband microstrip antenna resonating at 1.3 GHz is used as a microwave sensor. If human finger containing a specific value of the BGL is placed on a radiating patch of narrowband microstrip patch antenna, microwave sensor structure, then the near field of this radiating patch antenna structure gets interact with human finger and results change in electrical characteristics of the antenna. These electric changes are in relation to blood permittivity changes due to variation in BGL value. The change in electric characteristics of narrow band antenna microwave structure results corresponding frequency shift. The dataset of more than 200 individuals are generated where corresponding frequency shifts for various BGLs are recorded. The prepared dataset provided linear relationship between reference BGL and corresponding frequency shift. Regression analysis for BGL estimation resulted coefficient of determination with value 0.75. The significant improvement in this coefficient of determination is obtained by subband frequency analysis, which resulted in close approximation with expected value. The Surveillance Error Grid (SEG) and Mean Absolute Relative Difference (MARD) analyses are performed on prepared dataset to validate and verify clinical acceptance of microwave based non-invasive BGL estimation system. The developed antenna model has the MARD value of 4.204% and existing split ring resonator non-invasive antenna has 12.5% MARD value for the whole dataset.

12 citations

Journal ArticleDOI
TL;DR: In this paper , the authors focus on facial processing, which refers to artificial intelligence (AI) systems that take facial images or videos as input data and perform some AI-driven processing to obtain higher-level information (e.g. a person's identity, emotions, demographic attributes) or newly generated imagery.
Abstract: This work focuses on facial processing, which refers to artificial intelligence (AI) systems that take facial images or videos as input data and perform some AI-driven processing to obtain higher-level information (e.g. a person's identity, emotions, demographic attributes) or newly generated imagery (e.g. with modified facial attributes). Facial processing tasks, such as face detection, face identification, facial expression recognition or facial attribute manipulation, are generally studied as separate research fields and without considering a particular scenario, context of use or intended purpose. This paper studies the field of facial processing in a holistic manner. It establishes the landscape of key computational tasks, applications and industrial players in the field in order to identify the 60 most relevant applications adopted for real-world uses. These applications are analysed in the context of the new proposal of the European Commission for harmonised rules on AI (the AI Act) and the 7 requirements for Trustworthy AI defined by the European High Level Expert Group on AI. More particularly, we assess the risk level conveyed by each application according to the AI Act and reflect on current research, technical and societal challenges towards trustworthy facial processing systems.

7 citations

Journal ArticleDOI
TL;DR: In this article, the authors developed a solution to help the task force using a face recognition based UAV to identify the criminals, missing people, civilians and for surveillance, which is a technology which involves the understanding of how the faces are detected and recognized.

4 citations

Journal ArticleDOI
08 Feb 2023-Axioms
TL;DR: In this paper , the authors used Haar-cascade classifiers in combination with LBPH algorithm to implement real-time facial recognition for face recognition in a remotely piloted robot.
Abstract: The main objective of this unmanned ground vehicle is to deal with the security issues like terrorist activities across the border and in various remote combat missions by reducing the involvement of soldiers. This unmanned ground robot comprises a wireless high-definition camera that can transfer live streams from the robot to headquarters using Wi-Fi. The robot’s movement can be controlled with two modes; one of them is a radio controller working on 2.4 GHz frequency with seven independent channels. Secondly, its movement can also be controlled using a Python-based GUI application. Nowadays, different techniques have been used for face recognition; in our remotely piloted robot, we have used Haar-cascade classifiers in combination with the LBPH algorithm to implement real-time facial recognition. The robot uses a rack and pinion driving mechanism and an ATMEL Cortex-M3 CPU as a controller with 32-bit/s processing speed. In addition, a laser is installed on the turret to shoot the targets down, which can be used in an autonomous mode based on facial recognition results, or it can be used manually either through an RF controller or Python-based GUI. The turret moves in 2-DOF with the help of metallic geared servo motors. Both servo motors can rotate up to 180°. The driving mechanism of the robotic tank is just like DDR, with one difference, the two DC gear motors of the robot are connected diagonally.

3 citations