Absolute Face Recognition System using Machine Learning Approach from Blurred Images

被引:0
作者
Hemavathi, D. [1 ]
Tambe, Utkarsh Yashwant [1 ]
Hsiung, Pao-Ann [2 ]
机构
[1] SRM Inst Sci & Technol, Dept Data Sci & Business Syst, Chennai, Tamil Nadu, India
[2] Natl Chung Cheng Univ, Dept Comp Sci & Informat Engn, Chiayi, Taiwan
来源
2024 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATION AND APPLIED INFORMATICS, ACCAI 2024 | 2024年
关键词
face recognition; linear binary pattern; blurred images; machine learning; viola-jones algorithm; KNN;
D O I
10.1109/ACCAI61061.2024.10602036
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Face Recognition is an important task in many domains to obtain the exact image from the pool of images. In general, blurred images are extremely challenging for the sensitive areas like law and order, defense, etc. Many face recognition techniques work well in normal images with various dimensions. In sensitive domains, the blurred image act as key evidence for the entire scenario. So, it is very important to find the original and accurate image from the blurred images. Representation of face, extraction of features and classification are the important steps in face recognition process. In existing models, Linear Binary Pattern (LBP) methods are used to recognize accurate images. LBP with Histogram (LBPH) is used to improve the detection performance of original images. Image sorting is done using the Point Spread Function Estimation on the blurred region and it helped to recognize faces with more accuracy. Extended Uniform Linear Binary Pattern method is used to reduce the dimensions to concentrate more on center pixels, with the use of the Viola-Jones algorithm and K-nearest neighbor (KNN) classifier for prediction.The proposed enhanced LBP approach assisted in achieving 94.7% accuracy in recognizing human faces from blurry images.
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页数:7
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