Indian Journal of Artificial Intelligence and Neural Networking (IJAINN) ISSN: 2582-7626 (Online), Volume-1 Issue-3, June 2021 12 Retrieval Number: 100.1/ijainn.B1027021221 DOI:10.35940/ijainn.B1027.061321 Journal Website: www.ijainn.latticescipub.com Published By: Lattice Science Publication © Copyright: All rights reserved. A Hybrid Enhanced Real-Time Face Recognition Model using Machine Learning Method with Dimension Reduction Jaya Kumari, Kailash Patidar, Gourav Saxena, Rishi Kushwaha Abstract:Face recognition techniques play a crucial role in numerous disciplines of data security, verification, and authentication. The face recognition algorithm selects a face attribute from an image datasets. Recognize identification is an authentication device for verification as well as validation having both data analysis and feasible significance. The face- recognizing centered authentication framework can further be considered an AI technology implementation for instantly identifying a particular image. In this research, we are presenting a hybrid face recognition model (HFRM) using machine learning methods with “Speed Up Robust Features” (SURF), “scale-invariant feature transform” (SIFT), Locality Preserving Projections (LPP) &Principal component analysis (PCA) method. In the proposed HFRM model SURF method mainly detects the local feature efficiently. SIFT method mainly utilizes to detect the local features and recognize them. LPP retains the local framework of facial feature area which is generally quite meaningful than on the sequence kept by a 'principal component analysis (PCA) as well as “linear discriminate analysis” (LDA). The proposed HFRM method is compared with the existing (H. Zaaraoui et al., 2020) method and the experimental result clearly shows the outstanding performance in terms of detection rate and accuracy % over existing methods. Keyword: Speed up Robust Features, Hybrid Face Recognition Model, Linear Discriminate Analysis, PCA, LPP I. INTRODUCTION In the last decade, it can be observed that many algorithms were developed in image processing for face recognition and face detection but there was no algorithm for detecting a position in an image. The position detection in the controlled environment can be used in many environments where the positions are fixed. Manuscript received on April 03, 2021. Revised Manuscript received on May 21, 2021. Manuscript published on June 10, 2021. * Correspondence Author Jaya Kumari*, M.Tech Scholar, Department of Computer Science, School of Engineering, Sri Satya Sai University of Technology & Medical Sciences, Sehore, Madhya Pradesh, India. Email: Pathak.jayak@gmail.com Kailash Patidar, Assistant Professor, Department of Computer Science, School of Engineering, Sri Satya Sai University of Technology & Medical Sciences, Sehore, Madhya Pradesh, India. Email: kailsashpatidar123@gmail.com Mr. Gourav Saxena, Assistant Professor, Department of Computer Science, School of Engineering, Sri Satya Sai University of Technology & Medical Sciences, Sehore, Madhya Pradesh, India. Email: gauravsss1999@gmail.com Mr. Rishi Kushwaha, Assistant Professor, Department of Computer Science, School of Engineering, Sri Satya Sai University of Technology & Medical Sciences, Sehore, Madhya Pradesh, India. Email: rishisinghkushwah@gmail.com © The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/ ) The face is a particularly deformable object, and facial expressions are available in an extensive form of viable configurations. Time-various adjustments encompass boom and elimination of facial hair, wrinkles, and sagging of the pores and skin resulting from growing older and change in pores and skin color because of publicity to sunlight. Artifact-related modifications encompass cuts, scrapes, and bandages from injuries and style-associated problems like makeup, rings, and piercings. It needs to be pretty clear that the human face is a whole lot extra tough to model and recognize than maximum industrial elements [1,2]. The face recognition [3,4] procedure is prompted through numerous elements consisting of a form, reflectance, pose, occlusion, and illumination. A human face is an incredibly complex object with functions that can vary over the years, every so often very hastily. In this research, we are presenting a hybrid face recognition model (HFRM) using machine learning methods. This complete paper is organized in various sections which cover face recognition, application and challenges, related work, proposed HFRM model, simulation details, experimental results and finally covers conclusion and future work. II. FACE RECOGNITION, APPLICATION, AND CHALLENGES Face detection is a technology that determines the sizes and locations of human faces in digital images. It recognizes faces and ignores anything else, such as trees, bodies, and buildings. Face detection might be recognized as a more general instance of face confinement. It is the center of all facial analysis, e.g., face localization, and face recognition, face authentication, facial feature detection, face tracking, and facial expression recognition. Additionally, it is essential strategies for all different requisitions, for example, feature conferencing, substance-based picture recovery, and adroit human-machine cooperation (HCI) (S. Bhatia et al., 2019). 2.1Types of face recognition methods-Face recognition and detection have been one of the most studied topics in computer vision literature. It uses the following methods (S. S. Ali, et al., 2018). Table 1.1 is showing the types of recognition methods.