Study of Face Recognition Techniques: A Survey

Lal, Madan; Kumar, K. P. Sanal; Arain, Rafaqat Hussain; Maitlo, Abdullah; Ruk, Sadaquat Ali; Shaikh, Hidayatullah

doi:10.14569/ijacsa.2018.090606

Cited by 56 publications

(26 citation statements)

References 10 publications

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“…A comparative study of the analysis results obtained by different researchers by applying face recognition techniques on the basis of Eigen Faces is given in Table 1 [12]. OpenCV has three built-in face recognizers and thanks to its clean coding, we can use any of them just by changing a single line of code.…”

Section:  Model-based Methodsmentioning

confidence: 99%

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An Accurate System for Face Detection and Recognition

Ali

El‐Hafeez

Mohany

2019

JAMCS

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During the last few years, Local Binary Patterns (LBP) has aroused increasing interest in image processing and computer vision. LBP was originally proposed for texture analysis, and has proved a simple yet powerful approach to describe local structures. It has been extensively exploited in many applications, for instance, face image analysis, image and video retrieval, environment modeling, visual inspection, motion analysis, biomedical and aerial image analysis, remote sensing. Face recognition is an interesting and challenging problem, and impacts important applications in many areas such as identification for law enforcement, authentication for banking and security system access, and personal identification among others. In this paper we are concerned with face recognition in a video stream using Local Binary Pattern histogram with processed data. First we will detect faces by using a combination of Haar cascade files that uses skin detection, eye detection and nose detection as input of LBP to increase the accuracy of the proposed recognition system. Also, our system can be used to build a dataset of faces and names to be used in a recognition step. The experimental results have shown that the proposed system can achieve accuracy of recognition up to 96.5% which was better than the relevant methods.

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Section:  Model-based Methodsmentioning

confidence: 99%

“…This produces dimension reduction by allowing the smaller set of basis images to represent the original training images. We could achieve a good Classification by comparing how faces are represented by the basis set [12].…”

Section: Face Recognition Using Eigenfacesmentioning

confidence: 99%

An Accurate System for Face Detection and Recognition

Ali

El‐Hafeez

Mohany

2019

JAMCS

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“…Recent survey [10] yield SVM as still popular and efficient in terms of face recognition with the accuracy over 95 percent.…”

Section: Support Vector Machinementioning

confidence: 99%

“…NN is utilized in various areas of research, in terms of face recognition, the survey [10] indicates its accuracy to over 98 percent.…”

Section: Neural Networkmentioning

confidence: 99%

Raspberry Pi-Based Access Control Using Face Recognition

Kainz¹,

Drozd²,

Michalko³

et al. 2020

AEI

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The aim of the research is the proposal and experimental implementation of a solution based on Raspberry Pi that is to allow face recognition with subsequent user notification. Proposal of whole system discusses the possible negative factors that can endanger functionality of the system. Once the face is recognized or not recognized, the message is sent to the administrator and access is permitted or denied. This represents the elevated level of system hardening. The system in this research is built on histogram of oriented gradients (HOG) and neural network (NN). Testing of the software system solution has been also carried out and proves the usability with detection and recognitions rates above 90%. The implemented system may be used as a part of complex IoT (Internet of Things) security solution.

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“…In their day by day life, individuals only here and there show peak of their outward appearance during normal correspondence with their peers, except if for very specific cases and for extremely concise periods of time. The programmed outward appearance acknowledgment framework incorporates: [9,10] The facial feature recognition system has the following blocks:…”

Section: Fig 1: Architecturementioning

confidence: 99%

An IOT Method for Reducing Classification Error In Face Recognition With The Commuted Concept Of Conventional Algorithm

Kumar¹,

Rathore²,

Dutt³

2019

IJITEE

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The initial work has discussed the conventional approach of algorithms along with their drawbacks and features. Apart from that types of face recognition methodologies have been discussed with application of IOT trends. We specifically depicts a descriptive idea about working and applications of all conventional algorithms which have been commuted concept wise in proposed methodologies section of our work Our work consists of literature survey so we can provide a reason for the previous work and get basic ground for performing and implementing proposed work. One of a common procedures of face detection has been discussed that’s has been worked out in past with accuracy .The observation in this work leads to propose the method by commuting the conventional algorithm, Basically the work done with conventional approach has been discussed in this section with a strong focus over the role of Iot in face recognition and what is importance of Iot in this domain and what changes Iot concept has bring about as far as face recognition with different approach has been concerned . Not only PCA concept has been commuted but along with Pca, Svm, naïve bayes classifier, DCT, Gabor, neural network efficiency and their combined effect has been performed and analyzed later. Our work has been focusing around commuted concept of conventional algorithms so this particular chapter is very much important to discuss the conventional methodologies perform by classical mathematically implemented techniques for classifications. With the help of the analysis we will discuss the problem formulation and comparison of proposed work with existing work .So our work is basically about the problem existing with conventional algorithm for classifications and what lead us to propose the commuted concept further to deal or minimize the effect of that particular problem ,Our work is not primarily based on face recognition but to calculate the classification error through conventional algorithm and then compare it with our proposed commuted concept and combined effect of conventional algorithms as well, like PCA+SVM PCA+ Kernel SVM, Commuted Concept of PCA +Naïve bayes Classifier .We have gone through with different cases to ensure the minimization of classification error through proposed method .The goal of the work is to associate the application of

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Study of Face Recognition Techniques: A Survey

Cited by 56 publications

References 10 publications

An Accurate System for Face Detection and Recognition

An Accurate System for Face Detection and Recognition

Raspberry Pi-Based Access Control Using Face Recognition

An IOT Method for Reducing Classification Error In Face Recognition With The Commuted Concept Of Conventional Algorithm

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