Hybrid approaches to frontal view face recognition using the neural network

2002 ◽  
Author(s):  
Kang Sik Yoon ◽  
Young Kug Ham ◽  
Rae-Hong Park
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Frontal View ◽  
Hybrid Approaches ◽  
The Neural Network

The neural network for face recognition: Insights from an fMRI study on developmental prosopagnosia

NeuroImage ◽  
2018 ◽  
Vol 169 ◽  
pp. 151-161 ◽  
Author(s):  
Yuanfang Zhao ◽  
Zonglei Zhen ◽  
Xiqin Liu ◽  
Yiying Song ◽  
Jia Liu
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
The Neural Network ◽  
Fmri Study

Adaptive GLOH with PSO-trained NN for the recognition of plastic surgery faces and their types

2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Archana Harsing Sable ◽  
Sanjay N. Talbar
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Plastic Surgery ◽  
Local Binary Pattern ◽  
Principal Component ◽  
Traditional Learning ◽  
Swarm Optimization ◽  
The Neural Network ◽  
Orientation Histogram ◽  
The Face

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.

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

Connectivity ◽  
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.

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

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.

Face Recognition Classifier Design Based on the Genetic Algorithm and Neural Network

2014 ◽  
Vol 998-999 ◽  
pp. 869-872
Author(s):  
Na Li ◽  
Peng He ◽  
Qian Zhao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Face Recognition ◽  
Training Time ◽  
Classifier Design ◽  
The Neural Network ◽  
The Face ◽  
Feature Match ◽  
The One ◽  
And Training

In the course of the face feature match, many classifiers have been designed. The neural network is usually selected as a classifier because of its validity and universality, whereas its training time, training epochs, and its convergence, all are not satisfied to us. It is often influenced by the author’s experience. In the case, a collaborative genetic algorithm and neural network is presented as a new face recognition classifier. The one thing is to train the NN weights by the GA until the stopping criterion is met, and the next thing is to use the BP algorithm to continue to train the network. The training time and training epochs have been improved in the experiment of the face recognition on ORL face database. The simulation shows the validity of methods.

scholarly journals PENGGUNAAN MODEL HIDDEN MARKOV DAN METODE NEURAL NETWORK SEBAGAI PENERAPAN TEKNOLOGI PENGENALAN WAJAH

2019 ◽  
Vol 2 (1) ◽  
pp. 13
Author(s):  
Aria Hendrawan ◽  
Basworo Ardi Pramono ◽  
Whisnumurti Adhiwibowo
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Markov Models ◽  
Hidden Markov ◽  
Recognition System ◽  
Hybrid Approaches ◽  
Image Characteristics ◽  
Face Recognition System ◽  
The Face ◽  
Sequential Images

The human face recognition system is one of the fields that is quite developed at this time, where applications can be applied in the field of security (security system) such as permission to access room, surveillance (surveillance), as well as the search for individual identities in the police database. The face recognition approach aims to detect faces in 2-dimensional images and sequential images of videos that have many methods such as local, global, and hybrid approaches.  Hidden Model Markov (HMM) is another promising method that works well for images with different lighting variations, facial expressions, and orientations. HMM is a set of statistical models used to characterize signal properties. An artificial neural network-based approach is learned from image examples and relies on techniques from machine learning to find relevant facial image characteristics. The characteristics studied were in the form of discriminant functions (ie non-linear decision surfaces), then used for face recognition. In this study there will be an application to compare Hidden Markov Models and Neural Network Method as a Face Recognition Technology Algorithm Model.  

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