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A Hybrid Approach based on Haar Cascade, Softmax, and CNN for Human Face Recognition

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Title A Hybrid Approach based on Haar Cascade, Softmax, and CNN for Human Face Recognition
 
Creator Singh, Pancham
Kansal, Mrignainy
Singh, Rajeev
Kumar, Sushil
Sen, Chelsi
 
Subject Biometric system
Computer vision
Linear discriminant analysis
Principal component analysis
Viola-Jonas
 
Description 414-423
Face recognition has been studied long but it is still an important and current research field in deep learning, computer
vision, and forensics. There are several applications such as group action systems, human-machine interaction, and security
systems, where face recognition is of vital importance. It is noticed that the algorithms based on Deep Learning (DL) have
shown higher performances, stipulation of accuracy, and processing speed as compared to traditional machine learning
algorithms. With its dominant methodology in deep learning, the Convolutional Neural Network (CNN) has contributed
immensely to face recognition. In this paper, a novel hybrid version of the deep learning algorithm containing Haar Cascade,
SoftMax, and CNN components is proposed. It provides promising results for applications based on the recognition of
human faces. In the experiments, the accuracy of this hybrid algorithm is achieved at 99.95%, which is significantly higher
than existing Viola-Jonas and Principal Component Analysis (PCA), which have accuracy rates of 74.38% and 81.81%
respectively. However, the accuracy of our proposed algorithm close to Linear Discriminant Analysis (LDA) at 95.45%, and
SoftMax and CNN at 94%. In this paper, the proposed hybrid deep learning algorithm improves the result performance and
is compared with some existing techniques for face recognition.
 
Date 2024-04-09T11:21:11Z
2024-04-09T11:21:11Z
2024-04
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/63727
https://doi.org/10.56042/jsir.v83i4.3167
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.83(4) [April 2024]