Over the past few decades, interest in theories and algorithms for face recognition has been growing rapidly. Video surveillance, criminal identification, building access control, and unmanned and autonomous vehicles are just a few examples of concrete applications that are gaining attraction among industries. Various techniques are being developed including local, holistic, and hybrid approaches, which provide a face image description using only a few face image features or the whole facial features. The main contribution of this survey is to review some well-known techniques for each approach and to give the taxonomy of their categories. In the paper, a detailed comparison between these techniques is exposed by listing the advantages and the disadvantages of their schemes in terms of robustness, accuracy, complexity, and discrimination. One interesting feature mentioned in the paper is about the database used for face recognition. An overview of the most commonly used databases, including those of supervised and unsupervised learning, is given. Numerical results of the most interesting techniques are given along with the context of experiments and challenges handled by these techniques. Finally, a solid discussion is given in the paper about future directions in terms of techniques to be used for face recognition.

https://www.mdpi.com/1424-8220/20/2/342/pdf

Face Recognition Systems: A Survey

Part One. Multivariate distributions. Preliminary data analysis. Part Two: Finding new underlying variables. Principal component analysis. Factor analysis. Part Three: Procedures based on the multivariate normal distribution. The multivariate normal distribution. Procedures based on normal distribution theory. The multivariate analysis of variance. The multivariate analysis of covariance and related topics. Part Four: Multi-dimensional scaling and cluster analysis. Multi-dimensional scaling. Cluster analysis.

Introduction to multivariate analysis, by C. Chatfield and A. J. Collins. Pp 246. £13 hardcover, £7·50 paperback. 1980. ISBN 0-412-16030-7/4 (Chapman and Hall)

Facial Recognition is the biometric technique used in face detection. The task for validating or recognizing a face from the multi-media photographs is done using facial recognition technique. With the evolution of advanced society the requirement for face identification has been really important. Detection and identification of faces has been grown worldwide. It owes the demand for security such as authorization, national safety and other vital circumstances. There are number of algorithms for facial detection. This paper aspires to present the comparison of two face recognition techniques Haar Cascade and Local Binary Pattern edified for the classification. As a result the accuracy of Haar Cascade is more than the Local Binary Pattern but the execution time in Haar Cascade is more than Local Binary Pattern.

Facial recognition using Haar cascade and LBP classifiers

The growing number of services that can meet the users’ functional requirements, inspired many researchers to provide some approaches to rank and select the best possible services regarding their quality of service (QoS) and users’ preferences. Considering various criteria which should be considered in the service selection process, multi-criteria decision making (MCDM) techniques have been vastly applied to help a decision-maker in determining the weight of each QoS factor and ranking the services provided by different service providers. This paper provides an extensive investigation of the state of the art MCDM-based service selection schemes proposed in the literature. It provides the required background knowledge and puts forward a taxonomy of the investigated service selection schemes regarding their applied MCDM methods. Also, it describes how the MCDM methods are adapted by the studied schemes, which datasets and QoS criteria are employed by each system, and which factors and environments are utilized to evaluate the service selection schemes. Finally, the concluding remarks are provided, and directions for future studies are highlighted.

Service Selection Using Multi-criteria Decision Making: A Comprehensive Overview

Intelligent visual surveillance systems are attracting much attention from research and industry. The invention of smart surveillance cameras with greater processing power has now been the leading stakeholder, making it conceivable to design intelligent visual surveillance systems. It is possible to assure the safety of people in both homes and public places. This work aims to distinguish the suspicious activities for surveillance environments. For this, a 63 layers deep CNN model is suggested and named “L4-Branched-ActionNet”. The suggested CNN structure is centered on the alteration of AlexNet with added four blanched sub-structures. The developed framework is first transformed into a pre-trained framework by conducting its training on an object detection dataset called CIFAR-100 with the SoftMax function. The dataset for suspicious activity recognition is then forwarded to this pretrained model for feature acquisition. The acquired deep features are subjected to feature subset optimization. These extracted features are first coded by applying entropy and then an ant colony system (ACS) is utilized on the entropy-based coded features for optimization. The configured features are then fed into numerous SVM and KNN based classification models. The cubic SVM has the highest efficiency scores, with a performance of 0.9924 in order of accuracy. The proposed model is also validated on the Weizmann action dataset and attained an accuracy of 0.9796. The successful findings indicate the suggested work’s soundness.

/pdf/suspicious-activity-recognition-using-proposed-deep-l4-4h5vq97db6.pdf

Suspicious Activity Recognition Using Proposed Deep L4-Branched-Actionnet With Entropy Coded Ant Colony System Optimization

Recognition of human face is an important domain in unique identification of humans. It is currently being widely used in many industrial applications, such as video monitoring systems, human–computer interaction, and automatic gate control systems and for securing networks. Every university uses some method of attendance to keep a record of the number of students or people who attended that particular lecture. This paper delineates a method for taking attendance of people in a classroom which integrates the face recognition technology using local binary patterns histograms (LBPH) algorithm, along with face detection by Haar feature-based cascades and distance-based clustering. The proposed system records the attendance of the people in a classroom environment autonomously and provides the user with an output as a spreadsheet describing the attendance.

Automatic Attendance System Using Face Recognition Technique

Fractional Aquila spider monkey optimization based deep learning network for classification of brain tumor

Neural Based QoS aware Mobile Cloud Service and Its Application to Preeminent Service Selection using Back Propagation

The business framework architecture and evaluation are changing the growth of mobile cloud computing (MCC). The business service users required the facilities for selecting the mobile cloud services according to their quality of service (QoS) values in the business environment. In such a business environment, QoS parameter values help to build consumer confidence and provide a reliable environment for them. So the proposed system suggests a hybrid based on TOPSIS algorithm and plurality voting method which is more efficient, trustable for selecting the best cloud services. This algorithm has three phases to identify the service in the ranking process. The first phase is used to make a group the parameters based on the user’s requirements. The second phase applied the TOPSIS method on each of the parameters to get the service ranking. The final phase employed the plurality method which is counted the voting for each service to determine the best services. The hybrid algorithm takes O(n2) time in the best service selection process which is better than existing popular AHP, ANP multi-criteria decision methods in terms of time complexity.

A QoS-Aware Hybrid TOPSIS–Plurality Method for Multi-criteria Decision Model in Mobile Cloud Service Selection

The cloud service is communicating between mobile devices through the Internet. They are prepended as platform independent and loosely conjoined. This paper’s objective is to obtain effective services with composition of services based on specific QoS parameters using principal component factor analysis method. The Factor analysis, analyzing the interrelationships among variables by characterizing highly correlated known as factors. In factor analysis, particularly statistical principal component analysis is used to consider the interrelationships among variables with a minimal loss of information. This analysis is made from the data collected from the cloud service providers. This result is based on components of cloud service availability, response time, throughput, reliability, successability, compliance, best practices, and web services resource framework. These are few parameters considered among the QoS parameters to find the best mobile cloud services. In this factor analysis based on principal component analysis considers these eight parameters which can be further improved to select the cloud services of QoS.

Rajeshkannan Regunathan

Papers

Automatic Attendance System Using Face Recognition Technique

Fractional Aquila spider monkey optimization based deep learning network for classification of brain tumor

Neural Based QoS aware Mobile Cloud Service and Its Application to Preeminent Service Selection using Back Propagation

A QoS-Aware Hybrid TOPSIS–Plurality Method for Multi-criteria Decision Model in Mobile Cloud Service Selection

Evaluation of Quality of Service Parameters for Mobile Cloud Service Using Principal Component Analysis