A REVIEW ON STATE-OF-THE-ART FACE RECOGNITION APPROACHES

Fractals ◽  
2017 ◽  
Vol 25 (02) ◽  
pp. 1750025 ◽  
Author(s):  
ZAHID MAHMOOD ◽  
NAZEER MUHAMMAD ◽  
NARGIS BIBI ◽  
TAUSEEF ALI
Keyword(s):  
Face Recognition ◽  
State Of The Art ◽  
Recognition Rate ◽  
Research Problem ◽  
Critical Discussion ◽  
The Past ◽  
Open Research ◽  
Comprehensive Survey ◽  
Facial Images ◽  
Enormous Number

Automatic Face Recognition (FR) presents a challenging task in the field of pattern recognition and despite the huge research in the past several decades; it still remains an open research problem. This is primarily due to the variability in the facial images, such as non-uniform illuminations, low resolution, occlusion, and/or variation in poses. Due to its non-intrusive nature, the FR is an attractive biometric modality and has gained a lot of attention in the biometric research community. Driven by the enormous number of potential application domains, many algorithms have been proposed for the FR. This paper presents an overview of the state-of-the-art FR algorithms, focusing their performances on publicly available databases. We highlight the conditions of the image databases with regard to the recognition rate of each approach. This is useful as a quick research overview and for practitioners as well to choose an algorithm for their specified FR application. To provide a comprehensive survey, the paper divides the FR algorithms into three categories: (1) intensity-based, (2) video-based, and (3) 3D based FR algorithms. In each category, the most commonly used algorithms and their performance is reported on standard face databases and a brief critical discussion is carried out.

Machine Learning into Metaheuristics

2021 ◽  
Vol 54 (6) ◽  
pp. 1-32
Author(s):  
El-Ghazali Talbi
Keyword(s):  
Machine Learning ◽  
Optimization Problem ◽  
State Of The Art ◽  
Research Topic ◽  
High Quality ◽  
Research Issues ◽  
The Past ◽  
Open Research ◽  
Comprehensive Survey ◽  
High Level

During the past few years, research in applying machine learning (ML) to design efficient, effective, and robust metaheuristics has become increasingly popular. Many of those machine learning-supported metaheuristics have generated high-quality results and represent state-of-the-art optimization algorithms. Although various appproaches have been proposed, there is a lack of a comprehensive survey and taxonomy on this research topic. In this article, we will investigate different opportunities for using ML into metaheuristics. We define uniformly the various ways synergies that might be achieved. A detailed taxonomy is proposed according to the concerned search component: target optimization problem and low-level and high-level components of metaheuristics. Our goal is also to motivate researchers in optimization to include ideas from ML into metaheuristics. We identify some open research issues in this topic that need further in-depth investigations.

scholarly journals Elderly Fall Detection with an Accelerometer Using Lightweight Neural Networks

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1354 ◽  
Author(s):  
Gaojing Wang ◽  
Qingquan Li ◽  
Lei Wang ◽  
Yuanshi Zhang ◽  
Zheng Liu
Keyword(s):  
Neural Networks ◽  
Computational Cost ◽  
Fall Detection ◽  
The Elderly ◽  
Research Problem ◽  
Wearable Devices ◽  
The Past ◽  
Machine Learning Methods ◽  
Open Research ◽  
Different Types

Falls have been one of the main threats to people’s health, especially for the elderly. Detecting falls in time can prevent the long lying time, which is extremely fatal. This paper intends to show the efficacy of detecting falls using a wearable accelerometer. In the past decade, the fall detection problem has been extensively studied. However, since the hardware resources of wearable devices are limited, designing highly accurate embeddable models with feasible computational cost remains an open research problem. In this paper, different types of shallow and lightweight neural networks, including supervised and unsupervised models are explored to improve the fall detection results. Experiment results on a large open dataset show that the lightweight neural networks proposed have obtained much better results than machine learning methods used in previous work. Moreover, the storage and computation requirements of these lightweight models are only a few hundredths of deep neural networks in literature. In tested lightweight neural networks, the best one is proved to be the supervised convolutional neural network (CNN) that can achieve an accuracy beyond 99.9% with only 441 parameters. Its storage and computation requirements are only 1.2 KB and 0.008 MFLOPs, which make it more suitable to be implemented in wearable devices with restricted memory size and computation power.

scholarly journals Deep Learning for Cyber Security Applications: A Comprehensive Survey

2021 ◽  
Author(s):  
vinayakumar R ◽  
Mamoun Alazab ◽  
Soman KP ◽  
Sriram Srinivasan ◽  
Sitalakshmi Venkatraman ◽  
...  
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Cyber Security ◽  
Smart Cities ◽  
Critical Discussion ◽  
Future Research ◽  
Next Generation ◽  
Security Applications ◽  
The Past ◽  
Comprehensive Survey

Deep Learning (DL), a novel form of machine learning (ML) is gaining much research interest due to its successful application in many classical artificial intelligence (AI) tasks as compared to classical ML algorithms (CMLAs). Recently, DL architectures are being innovatively modelled for diverse applications in the area of cyber security. The literature is now growing with DL architectures and their variations for exploring different innovative DL models and prototypes that can be tailored to suit specific cyber security applications. However, there is a gap in literature for a comprehensive survey reporting on such research studies. Many of the survey-based research have a focus on specific DL architectures and certain types of malicious attacks within a limited cyber security problem scenario of the past and lack futuristic review. This paper aims at providing a well-rounded and thorough survey of the past, present, and future DL architectures including next-generation cyber security scenarios related to intelligent automation, Internet of Things (IoT), Big Data (BD), Blockchain, cloud and edge technologies. <br>This paper presents a tutorial-style comprehensive review of the state-of-the-art DL architectures for diverse applications in cyber security by comparing and analysing the contributions and challenges from various recent research papers. Firstly, the uniqueness of the survey is in reporting the use of DL architectures for an extensive set of cybercrime detection approaches such as intrusion detection, malware and botnet detection, spam and phishing detection, network traffic analysis, binary analysis, insider threat detection, CAPTCHA analysis, and steganography. Secondly, the survey covers key DL architectures in cyber security application domains such as cryptography, cloud security, biometric security, IoT and edge computing. Thirdly, the need for DL based research is discussed for the next generation cyber security applications in cyber physical systems (CPS) that leverage on BD analytics, natural language processing (NLP), signal and image processing and blockchain technology for smart cities and Industry 4.0 of the future. Finally, a critical discussion on open challenges and new proposed DL architecture contributes towards future research directions.

scholarly journals Deep Learning for Cyber Security Applications: A Comprehensive Survey

2021 ◽  
Author(s):  
vinayakumar R ◽  
Mamoun Alazab ◽  
Soman KP ◽  
Sriram Srinivasan ◽  
Sitalakshmi Venkatraman ◽  
...  
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Cyber Security ◽  
Smart Cities ◽  
Critical Discussion ◽  
Future Research ◽  
Next Generation ◽  
Security Applications ◽  
The Past ◽  
Comprehensive Survey

Deep Learning (DL), a novel form of machine learning (ML) is gaining much research interest due to its successful application in many classical artificial intelligence (AI) tasks as compared to classical ML algorithms (CMLAs). Recently, DL architectures are being innovatively modelled for diverse applications in the area of cyber security. The literature is now growing with DL architectures and their variations for exploring different innovative DL models and prototypes that can be tailored to suit specific cyber security applications. However, there is a gap in literature for a comprehensive survey reporting on such research studies. Many of the survey-based research have a focus on specific DL architectures and certain types of malicious attacks within a limited cyber security problem scenario of the past and lack futuristic review. This paper aims at providing a well-rounded and thorough survey of the past, present, and future DL architectures including next-generation cyber security scenarios related to intelligent automation, Internet of Things (IoT), Big Data (BD), Blockchain, cloud and edge technologies. <br>This paper presents a tutorial-style comprehensive review of the state-of-the-art DL architectures for diverse applications in cyber security by comparing and analysing the contributions and challenges from various recent research papers. Firstly, the uniqueness of the survey is in reporting the use of DL architectures for an extensive set of cybercrime detection approaches such as intrusion detection, malware and botnet detection, spam and phishing detection, network traffic analysis, binary analysis, insider threat detection, CAPTCHA analysis, and steganography. Secondly, the survey covers key DL architectures in cyber security application domains such as cryptography, cloud security, biometric security, IoT and edge computing. Thirdly, the need for DL based research is discussed for the next generation cyber security applications in cyber physical systems (CPS) that leverage on BD analytics, natural language processing (NLP), signal and image processing and blockchain technology for smart cities and Industry 4.0 of the future. Finally, a critical discussion on open challenges and new proposed DL architecture contributes towards future research directions.

scholarly journals Multiresolution Face Recognition through Virtual Faces Generation Using a Single Image for One Person

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Hae-Min Moon ◽  
Min-Gu Kim ◽  
Ju-Hyun Shin ◽  
Sung Bum Pan
Keyword(s):  
Face Recognition ◽  
Home Environment ◽  
Smart Home ◽  
Euclidean Distance ◽  
Recognition Rate ◽  
Time Service ◽  
Surveillance Camera ◽  
Face Images ◽  
Smart Home Environment ◽  
Facial Images

In recent years, various studies have been conducted to provide a real-time service based on face recognition in Internet of things environments such as in a smart home environment. In particular, face recognition in a network-based surveillance camera environment can significantly change the performance or utilization of face recognition technology because the size of image information to be transmitted varies depending on the communication capabilities. In this paper, we propose a multiresolution face recognition method that uses virtual facial images by distance as learning to solve the problem of low recognition rate caused by communication, camera, and distance change. Face images for each virtual distance are generated through clarity and image degradation for each resolution, using a single high-resolution face image. The proposed method achieved a performance that was 5.9% more accurate than methods using MPCA and SVM, when LDA and the Euclidean distance were employed for a DB that was configured using faces that were acquired from the real environments of five different streets.

Data-driven Crowd Modeling Techniques: A Survey

2022 ◽  
Vol 32 (1) ◽  
pp. 1-33
Author(s):  
Jinghui Zhong ◽  
Dongrui Li ◽  
Zhixing Huang ◽  
Chengyu Lu ◽  
Wentong Cai
Keyword(s):  
Game Design ◽  
State Of The Art ◽  
Crowd Simulation ◽  
Data Driven ◽  
Future Research ◽  
Crowd Modeling ◽  
The Past ◽  
Modeling Techniques ◽  
Comprehensive Survey ◽  
Data Driven Modeling

Data-driven crowd modeling has now become a popular and effective approach for generating realistic crowd simulation and has been applied to a range of applications, such as anomaly detection and game design. In the past decades, a number of data-driven crowd modeling techniques have been proposed, providing many options for people to generate virtual crowd simulation. This article provides a comprehensive survey of these state-of-the-art data-driven modeling techniques. We first describe the commonly used datasets for crowd modeling. Then, we categorize and discuss the state-of-the-art data-driven crowd modeling methods. After that, data-driven crowd model validation techniques are discussed. Finally, six promising future research topics of data-driven crowd modeling are discussed.

scholarly journals A Review of Soft Computing Techniques for Gene Prediction

ISRN Genomics ◽  
2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Neelam Goel ◽  
Shailendra Singh ◽  
Trilok Chand Aseri
Keyword(s):  
Soft Computing ◽  
Genome Annotation ◽  
Gene Prediction ◽  
Research Problem ◽  
Future Research ◽  
Research Directions ◽  
The Past ◽  
Open Research ◽  
Soft Computing Techniques ◽  
Future Research Directions

In the past decade, various genomes have been sequenced in both plants and animals. The falling cost of genome sequencing manifests a great impact on the research community with respect to annotation of genomes. Genome annotation helps in understanding the biological functions of the sequences of these genomes. Gene prediction is one of the most important aspects of genome annotation and it is an open research problem in bioinformatics. A large number of techniques for gene prediction have been developed over the past few years. In this paper a theoretical review of soft computing techniques for gene prediction is presented. The problem of gene prediction, along with the issues involved in it, is first described. A brief description of soft computing techniques, before discussing their application to gene prediction, is then provided. In addition, a list of different soft computing techniques for gene prediction is compiled. Finally some limitations of the current research and future research directions are presented.

scholarly journals Anchors Bring Ease: An Embarrassingly Simple Approach to Partial Multi-View Clustering

2019 ◽  
Vol 33 ◽  
pp. 118-125 ◽  
Author(s):  
Jun Guo ◽  
Jiahui Ye
Keyword(s):  
Spectral Clustering ◽  
Time Complexity ◽  
State Of The Art ◽  
Research Problem ◽  
Simple Approach ◽  
Clustering Methods ◽  
Real World Data ◽  
The Past ◽  
Benchmark Datasets ◽  
In Virtue Of

Clustering on multi-view data has attracted much more attention in the past decades. Most previous studies assume that each instance appears in all views, or there is at least one view containing all instances. However, real world data often suffers from missing some instances in each view, leading to the research problem of partial multi-view clustering. To address this issue, this paper proposes a simple yet effective Anchorbased Partial Multi-view Clustering (APMC) method, which utilizes anchors to reconstruct instance-to-instance relationships for clustering. APMC is conceptually simple and easy to implement in practice, besides it has clear intuitions and non-trivial empirical guarantees. Specifically, APMC firstly integrates intra- and inter- view similarities through anchors. Then, spectral clustering is performed on the fused similarities to obtain a unified clustering result. Compared with existing partial multi-view clustering methods, APMC has three notable advantages: 1) it can capture more non-linear relations among instances with the help of kernel-based similarities; 2) it has a much lower time complexity in virtue of a noniterative scheme; 3) it can inherently handle data with negative entries as well as be extended to more than two views. Finally, we extensively evaluate the proposed method on five benchmark datasets. Experimental results demonstrate the superiority of APMC over state-of-the-art approaches.

Color Face Recognition Based on Quaternion Zernike Moment Invariants and Quaternion BP Neural Network

2013 ◽  
Vol 446-447 ◽  
pp. 1034-1039 ◽  
Author(s):  
Bei Jing Chen ◽  
Hua Zhong Shu ◽  
Gang Chen ◽  
Jun Ge
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Zernike Moment ◽  
Moment Invariants ◽  
Intensity Information ◽  
Active Research ◽  
Facial Images ◽  
Color Face Recognition

As an active research topic, many algorithms have been presented for face recognition. However, they mainly utilize the monochromatic intensity information. Among a few color face recognition methods, most of them treat the three channels separately. In this paper, a color face image is treated in a holistic manner by using the quaternion theory. We then propose a new algorithm for color face recognition, which uses the quaternion Zernike moment invariants and the quaternion BP neural network for the color face recognition. Experimental results on the Collection of Facial Images (Grimace) database, including major expression variation and considerable variation in head turn and tilt, show that the proposed method is better than the conventional ones in recognition rate.

Multi-pose 3D facial texture refinement for face recognition

2018 ◽  
Vol 16 (02) ◽  
pp. 1840006
Author(s):  
Wanshun Gao ◽  
Xi Zhao ◽  
Jun An ◽  
Jianhua Zou
Keyword(s):  
Face Recognition ◽  
State Of The Art ◽  
Competitive Performance ◽  
3D Face Reconstruction ◽  
3D Face ◽  
Face Reconstruction ◽  
Novel Approach ◽  
Art Methods ◽  
The Face ◽  
Facial Images

In this paper, we propose a novel approach for 3D face reconstruction from multi-facial images. Given original pose-variant images, coarse 3D face templates are initialized to reconstruct a refined 3D face mesh in an iteration manner. Then, we warp original facial images to the 2D meshes projected from 3D using Sparse Mesh Affine Warp (SMAW). Finally, we weight the face patches in each view respectively and map the patch with higher weight to a canonical UV space. For facial images with arbitrary pose, their invisible regions are filled with the corresponding UV patches. Poisson editing is applied to blend different patches seamlessly. We evaluate the proposed method on LFW dataset in terms of texture refinement and face recognition. The results demonstrate competitive performance compared to state-of-the-art methods.

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