Effective face recognition using deep learning based linear discriminant classification

Shailaja, K.; Anuradha, B.

doi:10.1109/iccic.2016.7919708

Cited by 16 publications

(7 citation statements)

References 7 publications

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“…Examinations are made among Basic LBP, Basic LTP, Basic MB-LBP, MB-LTP, ELBP, ELTP, and EMB-LBP as methodologies for face recognition. [10] The trial results on the Yale face database demonstrate that the proposed administrator EMB-LTP is similar with LTP and ELTP yet is better than the other tried methodologies. Since the Yale face database is little and basic, the prevalence of the proposed EMB-LTP remain not affirmed.…”

Section: Principle Component Analysismentioning

confidence: 86%

See 1 more Smart Citation

Comparative Analysis of Face Detection Using Linear Binary Techniques and Neural Network Approaches

Mughal¹,

Bhatti²,

Khuhawar³

et al. 2018

EAI Endorsed Transactions on Creative Technologies

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Facial recognition is now a days is very emerging topic. The most challenging task for normal human being is to follow the facial recognition retrieval model for correct match in the least running time. Especially while dealing with moving or non-static environment like live video, webcam recording, or accessing real-time video in which facial features are not clear as to take as input image. Comparison between two different approached has been presented in this paper, linear binary pattern Haar technique is compared by deep learning using neural networks, different images of different persons has been taken, deep learning approach is more accurate according to different angles the video taken or any distance the video captured either for moving or static objects either from Mobile Video Camera or CCTV Camera then LPBH approach.

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Section: Principle Component Analysismentioning

confidence: 86%

“…Examinations were directed on JAFFE female, CMU-PIE and FRGC version2 databases. The outcomes demonstrates that CS-LBP reliably [9], [10] In this paper, another methodology EMB-LTP for face recognition is introduced. Serious examinations are finished.…”

Section: Principle Component Analysismentioning

confidence: 98%

Comparative Analysis of Face Detection Using Linear Binary Techniques and Neural Network Approaches

Mughal¹,

Bhatti²,

Khuhawar³

et al. 2018

EAI Endorsed Transactions on Creative Technologies

View full text Add to dashboard Cite

show abstract

“…In their research article [29], authors have presented an active face recognition model using Deep Learning based linear discriminant classification (LDC). Proposed system was implemented using a matrix formed out of the database that corresponds to facial system comprising of columns and rows.…”

Section: A Comprehensive Review On Various Face Recognition System Usmentioning

confidence: 99%

A Study on various state of the art of the Art Face Recognition System using Deep Learning Techniques

Chokkadi¹,

Sudeepa

et al. 2019

IJATCSE

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Considering the existence of very large amount of available data repositories and reach to the very advanced system of hardware, systems meant for facial identification have evolved enormously over the past few decades. Sketch recognition is one of the most important areas that have evolved as an integral component adopted by the agencies of law administration in current trends of forensic science. Matching of derived sketches to photo images of face is also a difficult assignment as the considered sketches are produced upon the verbal explanation depicted by the eye witness of the crime scene and may have scarcity of sensitive elements that exist in the photograph as one can accurately depict due to the natural human error. Substantial amount of the novel research work carried out in this area up late used recognition system through traditional extraction and classification models. But very recently, few researches work focused on using deep learning techniques to take an advantage of learning models for the feature extraction and classification to rule out potential domain challenges. The first part of this review paper basically focuses on deep learning techniques used in face recognition and matching which as improved the accuracy of face recognition technique with training of huge sets of data. This paper also includes a survey on different techniques used to match composite sketches to human images which includes component-based representation approach, automatic composite sketch recognition technique etc.

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“…However, in this study, subject fatigue is not considered for cognitive load classification [ 11 ]. Five ML algorithms, support vector machine (SVM) [ 22 ], logistic regression (LR) [ 23 ], linear discriminant analysis (LDA) [ 24 ], k-nearest neighbor (k-NN) [ 25 ] and decision tree (DT) [ 26 ], are deployed for cognitive load classification. Further, three DL architectures: convolutional neural networks (CNN) [ 27 ], long-short-term-memory (LSTM) [ 28 , 29 ] and autoencoder (AE) [ 30 ] are designed both for automatic feature extraction from raw eye movement signals and for classification.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Driver’s Cognitive Load Classification Considering Eye Movement Using Machine Learning and Deep Learning

Rahman

Ahmed

Funk

et al. 2021

Sensors

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Due to the advancement of science and technology, modern cars are highly technical, more activity occurs inside the car and driving is faster; however, statistics show that the number of road fatalities have increased in recent years because of drivers’ unsafe behaviors. Therefore, to make the traffic environment safe it is important to keep the driver alert and awake both in human and autonomous driving cars. A driver’s cognitive load is considered a good indication of alertness, but determining cognitive load is challenging and the acceptance of wire sensor solutions are not preferred in real-world driving scenarios. The recent development of a non-contact approach through image processing and decreasing hardware prices enables new solutions and there are several interesting features related to the driver’s eyes that are currently explored in research. This paper presents a vision-based method to extract useful parameters from a driver’s eye movement signals and manual feature extraction based on domain knowledge, as well as automatic feature extraction using deep learning architectures. Five machine learning models and three deep learning architectures are developed to classify a driver’s cognitive load. The results show that the highest classification accuracy achieved is 92% by the support vector machine model with linear kernel function and 91% by the convolutional neural networks model. This non-contact technology can be a potential contributor in advanced driver assistive systems.

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Effective face recognition using deep learning based linear discriminant classification

Cited by 16 publications

References 7 publications

Comparative Analysis of Face Detection Using Linear Binary Techniques and Neural Network Approaches

Comparative Analysis of Face Detection Using Linear Binary Techniques and Neural Network Approaches

A Study on various state of the art of the Art Face Recognition System using Deep Learning Techniques

Vision-Based Driver’s Cognitive Load Classification Considering Eye Movement Using Machine Learning and Deep Learning

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