A Hybrid Enhanced Real-Time Face Recognition Model using Machine Learning Method with Dimension Reduction 
Jaya Kumari1, Kailash Patidar2, Gourav Saxena3, Rishi Kushwaha4
1Jaya Kumari, M.Tech Scholar, Department of Computer Science, School of Engineering, Sri Satya Sai University of Technology & Medical Sciences, Sehore, Madhya Pradesh, India.
2Kailash Patidar, Assistant Professor, Department of Computer Science, School of Engineering, Sri Satya Sai University of Technology & Medical Sciences, Sehore, Madhya Pradesh, India.
3Mr. Gourav Saxena, Assistant Professor, Department of Computer Science, School of Engineering, Sri Satya Sai University of Technology & Medical Sciences, Sehore, Madhya Pradesh, India.
4Mr. Rishi Kushwaha, Assistant Professor, Department of Computer Science, School of Engineering, Sri Satya Sai University of Technology & Medical Sciences, Sehore, Madhya Pradesh, India.
Manuscript received on 03 April 2021 | Revised Manuscript received on 21 May 2021 | Manuscript Accepted on 15 June 2021 | Manuscript published on 30 June 2021 | PP: 12-16 | Volume-1 Issue-3, June 2021 | Retrieval Number: 100.1/ijainn.B1027021221 | DOI: 10.54105/ijainn.B1027.061321
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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 facer cognizing 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.
Keywords: Speed up Robust Features, Hybrid Face Recognition Model, Linear Discriminate Analysis, PCA, LPP.
Scope of the Article: Data Mining and Machine Learning Tools