Faculty Opinions recommendation of The neural network for face recognition: Insights from an fMRI study on developmental prosopagnosia.

Abstract Numerous algorithms have met complexity in recognizing the face, which is invariant to plastic surgery, owing to the texture variations in the skin. Though plastic surgery serves to be a challenging issue in the domain of face recognition, the concerned theme has to be restudied for its hypothetical and experimental perspectives. In this paper, Adaptive Gradient Location and Orientation Histogram (AGLOH)-based feature extraction is proposed to accomplish effective plastic surgery face recognition. The proposed features are extracted from the granular space of the faces. Additionally, the variants of the local binary pattern are also extracted to accompany the AGLOH features. Subsequently, the feature dimensionality is reduced using principal component analysis (PCA) to train the artificial neural network. The paper trains the neural network using particle swarm optimization, despite utilizing the traditional learning algorithms. The experimentation involved 452 plastic surgery faces from blepharoplasty, brow lift, liposhaving, malar augmentation, mentoplasty, otoplasty, rhinoplasty, rhytidectomy and skin peeling. Finally, the proposed AGLOH proves its performance dominance.

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Development of an algorithm for object access system automation based on face recognition using a neural network

Connectivity ◽

10.31673/2412-9070.2020.033437 ◽

2020 ◽

Vol 145 (3) ◽

Author(s):

V. S. Orlenko ◽

◽

I. I. Kolosinsʹkyy

Keyword(s):

Neural Network ◽

Face Recognition ◽

Neural Model ◽

Large Datasets ◽

Second Step ◽

Test Set ◽

Access System ◽

The Neural Network ◽

The Face ◽

Face Search

The article deals with the technical side of face recognition — the neural network. The advantages of the neural network for identification of the person are substantiated, the stages of comparison of two images are considered. The first step is defined as the face search in the photo. Using several tests, the best neural network was identified, which allowed to effectively obtain a normalized image of a person’s face. The second step is to find the features of the person, for which the comparative analysis is performed. It was this stage that became the main point in this article — 16 sets of tests were carried out, each test set has 12 tests inside. Two large datasets were used for the study to evaluate the effectiveness of the algorithms not only in ideal circumstances but also in the field. The results of the study allowed us to determine the best method and neural model for finding a face and dividing it into parts. It is determined which part of the face the algorithm recognizes best — it will allow making adjustments to the location of the camera.

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The neural network sustaining crossmodal integration is impaired in alcohol-dependence: An fMRI study

Cortex ◽

10.1016/j.cortex.2012.04.012 ◽

2013 ◽

Vol 49 (6) ◽

pp. 1610-1626 ◽

Cited By ~ 27

Author(s):

Pierre Maurage ◽

Frédéric Joassin ◽

Mauro Pesenti ◽

Cécile Grandin ◽

Alexandre Heeren ◽

...

Keyword(s):

Neural Network ◽

Alcohol Dependence ◽

Crossmodal Integration ◽

The Neural Network ◽

Fmri Study

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The Neural Network Models Effectiveness for Face Detection and Face Recognition

2021 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream) ◽

10.1109/estream53087.2021.9431476 ◽

2021 ◽

Author(s):

Kirill Smelyakov ◽

Anastasiya Chupryna ◽

Oleksandr Bohomolov ◽

Nikita Hunko

Keyword(s):

Neural Network ◽

Face Recognition ◽

Face Detection ◽

Network Models ◽

Neural Network Models ◽

The Neural Network

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Way of application of convolutional neural networks for personality recognition and user emotions by keyboard handwriting

Legal, regulatory and metrological support of information security system in Ukraine ◽

10.20535/2074-9481.2(38).2019.232654 ◽

2021 ◽

pp. 9-17

Author(s):

Yurii Kulakov ◽

Liudmyla Tereikovska ◽

Ihor Tereikovskyi

Keyword(s):

Neural Network ◽

Face Recognition ◽

Convolutional Neural Network ◽

Structural Parameters ◽

Computer Experiments ◽

Training Sample ◽

Input Field ◽

The Neural Network ◽

Important Direction ◽

Personality Recognition

An important direction of increasing the security and expanding the functionality of modern information systems is the introduction of face recognition tools and user emotions by their keyboard handwriting. The expediency of improving the indicated recognition means by introducing modern neural network solutions into them is shown. A way has been developed for using a convolutional neural network for recognizing a user's face and emotions from keyboard handwriting, the features of which are the procedure for adapting the structural parameters of a convolutional neural network of the VGG type to the expected conditions of use and a procedure for determining the input field, which provides the representation of the parameters of colored channels. After adapting the structural parameters, the VGG network was implemented using the MATLAB R2018b application package, which made it possible to carry out computer experiments aimed at verifying the proposed method. As a result of the conducted computer experiments, it was determined that the use of the proposed method of applying a convolutional neural network makes it possible to achieve a user face recognition accuracy of about 82% with 50 learning epochs. The need for further research in the direction of the formation of a training sample is shown, which will ensure high-quality training of the neural network model.

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