Face Recognition Using Neural Network: A Review

Face detection is one of the most relevant applications of image processing and biometric systems. Artificial neural networks (ANN) have been used in the field of image processing and pattern recognition. There is lack of literature surveys which give overview about the studies and researches related to the using of ANN in face detection. Therefore, this research includes a general review of face detection studies and systems which based on different ANN approaches and algorithms. The strengths and limitations of these literature studies and systems were included also.

In this study, we present the performance of Support Vector Machine (SVM), Convolution Neural Network (CNN), and Artificial Neural Network (ANN) with Bag of Words (BoW), Histogram of Oriented Gradients (HOG), and Image Pixels (IP) for face recognition. SVM, CNN, and ANN are machine learning approaches and has been used for pattern recognition, especially in face recognition technology. BoW, HOG, and IP are being used for image feature extraction. The testing has been conducted from publicly available AT&T face database. Every individual subject consists of 10 images with different facial expression, different illumination and the dimensions of the images is unified as 92-by-112 pixels with PGM formats. SVM achieved recognition accuracy of 97.00%, 96.00%, and 98.00% with subsequently BoW, HOG, and IP. CNN achieved recognition accuracy of 94.00%, 99.00%, and 99.50% with subsequently BoW, HOG, and IP. ANN achieved recognition accuracy of 96.00%, 99.00%, and 99.50% with subsequently BoW, HOG, and IP. The experimental results show that the IP with ANN approach clearly outperformed the others approaches.

An exhaustive literature survey shows, that a very little effort has been done towards Artificial Neural Network (ANN) approach in the area of concrete technology [1, 2, 3]. In the present investigation, development of ANN approach for prognostication of concrete mix proportion in lieu of conventional laboratory approach. The traditional lab approach attracts some drawbacks such as lot of manual involvement, time consuming, chances of creeping of human errors, uncertain prediction and always invasive in nature. Hence to reduce above said drawbacks, this study is undertaken to develop a ANN between concrete mix ingredient properties namely maximum size of aggregate, degree of quality control, degree of workability, type of exposure, characteristic compressive strength required in the field at 28 days and concrete mix proportion. Prognostication of concrete mix proportion is essential for all structural works. The present work deals with collection of huge input data base from literatures, ANN’s training and its testing are adopted to fix the appropriate weighted matrix (Illustrated in Fig [1]) which in turn Prognosticates the appropriate concrete mix proportion. Indian standard code (IS 10262:1982) procedure is also adopted to compare the concrete mix proportions of same samples. The Prognosticated concrete mix proportion from ANN approach yielded very high accuracy results (As shown in fig [2]) compared with IS code method. To account for larger sample data the results of this work will contribute for the prognostication of concrete mix proportions up to a certain degree of level, which will assist a structural engineer in estimation of concrete mix proportion, with minimum effort and non- invasive technique.

Estimation of static formation temperatures in geothermal wells by using an artificial neural network approach

Gaining knowledge from health data may contribute to novel approaches or improved patient care plans. To intelligently acquire knowledge from the stroke data, machine learning (ML) technique is utilised and healthcare systems are used. Globally, security has become a major concern, and biometric technologies such as face detection and recognition technologies have been developed to solve security issues and security threats in the world. It has become possible because of developments in technology in the fields of automatic face analysis, machine learning, and pattern recognition. This chapter proposes a prototype to classify brain stroke and face recognition using ML techniques in the medical image. Face recognition and classification of stroke through ML technique like Decision Tree (DT), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Deep Learning with Convolution Neural Network (CNN) based on texture features and statistical features. A CNN model is experimentally used along with a collection of hyper-parameters for deep face recognition and stroke classification. Promising experimental results, with a total accuracy of 98.86% and 98.5%, are obtained, which demonstrates the effectiveness of deep face identification and stroke classification, respectively, in biometric systems.

Ecological Applications of Fuzzy Logic.- Ecological Applications of Qualitative Reasoning.- Ecological Applications of Non-supervised Artificial Neural Networks.- Ecological Applications of Genetic Algorithms.- Ecological Applications of Evolutionary Computation.- Ecological Applications of Adaptive Agents.- Bio-Inspired Design of Computer Hardware by Self-Replicating Cellular Automata.- Prediction and Elucidation of Stream Ecosystems.- Development and Application of Predictive River Ecosystem Models Based on Classification Trees and Artificial Neural Networks.- Modelling Ecological Interrelations in Running Water Ecosystems with Artificial Neural Networks.- Non-linear Approach to Grouping, Dynamics and Organizational Informatics of Benthic Macroinvertebrate Communities in Streams by Artificial Neural Networks.- Elucidation of Hypothetical Relationships between Habitat Conditions and Macroinvertebrate Assemblages in Freshwater Streams by Artificial Neural Networks.- Prediction and Elucidation of River Ecosystems.- Prediction and Elucidation of Population Dynamics of the Blue-green Algae Microcystis aeruginosa and the Diatom Stephanodiscus hantzschii in the Nakdong River-Reservoir System (South Korea) by a Recurrent Artificial Neural Network.- An Evaluation of Methods for the Selection of Inputs for an Artificial Neural Network Based River Model.- Utility of Sensitivity Analysis by Artificial Neural Network Models to Study Patterns of Endemic Fish Species.- Prediction and Elucidation of Lake and Marine Ecosystems.- A Comparison between Neural Network Based and Multiple Regression Models for Chlorophyll-a Estimation.- Artificial Neural Network Approach to Unravel and Forecast Algal Population Dynamics of Two Lakes Different in Morphometry and Eutrophication.- Hybrid Evolutionary Algorithm for Rule Set Discovery in Time-Series Data to Forecast and Explain Algal Population Dynamics in Two Lakes Different in Morphometry and Eutrophication.- Multivariate Time Series Prediction of Marine Zooplankton by Artificial Neural Networks.- Classification of Fish Stock-Recruitment Relationships in Different Environmental Regimes by Fuzzy Logic with Bootstrap Re-sampling Approach.- Computational Assemblage of Ordinary Differential Equations for Chlorophyll-a Using a Lake Process Equation Library and Measured Data of Lake Kasumigaura.- Classification of Ecological Images at Micro and Macro Scale.- Identification of Marine Microalgae by Neural Network Analysis of Simple Descriptors of Flow Cytometric Pulse Shapes.- Age Estimation of Fish Using a Probabilistic Neural Network.- Pattern Recognition and Classification of Remotely Sensed Images by Artificial Neural Networks.

Silk fibroin/xanthan composite was investigated as a suitable biomedical material for controlled drug delivery, and blending ratios of silk fibroin and xanthan were optimized by response surface methodology (RSM) and artificial neural network (ANN) approach. A non-linear ANN model was developed to predict the effect of blending ratios, percentage swelling and porosity of composite material on cumulative percentage release. The efficiency of RSM was assessed against ANN and it was found that ANN is better in optimizing and modeling studies for the fabrication of the composite material. In-vitro release studies of the loaded drug chloramphenicol showed that the optimum composite scaffold was able to minimize burst release of drug and was followed by controlled release for 5days. Mechanistic study of release revealed that the drug release process is diffusion controlled. Moreover, during tissue engineering application, investigation of release pattern of incorporated bioactive agent is beneficial to predict, control and monitor cellular response of growing tissues. This work also presented a novel insight into usage of various drug release model to predict material properties. Based on the goodness of fit of the model, Korsmeyer-Peppas was found to agree well with experimental drug release profile, which indicated that the fabricated material has swellable nature. The chloramphenicol (CHL) loaded scaffold showed better efficacy against gram positive and gram negative bacteria. CHL loaded SFX55 (50:50) scaffold shows promising biocomposite for drug delivery and tissue engineering applications.

This study deals with predicting performance parameters and exhaust emissions of a four-stroke, four-cylinder, direct injection diesel engine fuelled with soybean oil methyl ester and its blends with jet fuel, marine fuel and No. 2 diesel fuel using Artificial Neural Network (ANN) approach. For this aim, an ANN model for the engine was developed using experimental data, and the performance of the ANN predictions was measured by comparing them with the experimental results. It was revealed that the ANN approach can accurately predict the performance parameters and exhaust emissions of the diesel engine using various diesel and biodiesel fuels.

Artificial neural network approach for prediction of ammonia emission from field-applied manure and relative significance assessment of ammonia emission factors

Experimental and theoretical investigation of thermal conductivity of ethylene glycol containing functionalized single walled carbon nanotubes

This paper attempts to compare the weather indices based regression approach and Multilayer Perceptron (MLP) Artificial Neural Network (ANN) approach for rice yield prediction at district level of West Bengal. The weather indices for weather variables, viz., minimum temperature, maximum temperature, rainfall, and relative humidity are used as input variables along with time variable t and yield of rice as output variable. The study reveals that the ANN approach works better than the standard regression approach in crop yield prediction. The prediction error percentages are found to be consistently less than 5% in MLP ANN approach except for one district.

Sonophotocatalytic treatment of AB113 dye and real textile wastewater using ZnO/persulfate: Modeling by response surface methodology and artificial neural network

Effect of hBN and SDS added vegetable based cutting fluid application on the performance of turning Ti6Al4V alloys: A Comparative analysis with Taguchi and ANN approaches

Impact damage effects on the residual mechanical properties of the structure can be quite detrimental, and failure patterns depend strongly on the impact energy levels. This paper presents impact energy level assessment method using 2D multiple signal classification (MUSIC) and artificial neural network (ANN) approach for composite structures. Because uniform linear array has the shortcoming of the half-plane mirror effect, 2D MUSIC algorithm using a plum blossom sensor array is firstly applied to locate the position of impact and extract impact energy feature. Secondly, the relation impact energy feature, impact position, and impact energy level at different measurement points are established using ANN approach. Through the trained ANN, the impact energy level can be predicted when the position and impact energy feature results of unknown impact are given as input. Finally, the proposed method is applied to a quasi-isotropic epoxy laminate plate and a large stiffened carbon fiber composite structure showing its successful performance on composite structure. Copyright © 2015 John Wiley & Sons, Ltd.

The design goal of a control system is to influence the behavior of dynamic systems to achieve some pre-determinate objectives. A control system is usually designed on the premise that an accurate knowledge of a given object and environment cannot be obtained in advance. It usually requires suitable methods to address the problems related to uncertain and highly complicated dynamic system identification. As a matter of fact, system identification is an important branch of research in the automatic control domain. However, the majority of methods for system identification and parameters' adjustment are based on linear analysis: therefore it is difficult to extend them to complex non-linear systems. Normally, a large amount of approximations and simplifications have to be performed and, unavoidably, they have a negative impact on the desired accuracy. Fortunately the characteristics of the Artificial Neural Network (ANN) approach, namely non-linear transformation and support to highly parallel operation, provide effective techniques for system identification and control, especially for non-linear systems [1-9]. The ANN approach has a high potential for identification and control applications mainly because: (1) it can approximate the nonlinear input-output mapping of a dynamic system [10]; (2) it enables to model the complex systems’ behavior and to achieve an accurate control through training, without a priori information about the structures or parameters of systems. Due to these characteristics, there has been a growing interest, in recent years, in the application of neural networks to dynamic system identification and control.