Study of Different Face Recognition Algorithms and Challenges

Kurmi, Uma Shankar; Agrawal, Dheeraj; Baghel, Rishabh; Azad, Maulana

doi:10.17950/ijer/v3s2/216

Cited by 10 publications

(7 citation statements)

References 12 publications

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“…Lights, dimensions, and alignments may all be used to make these tactics invariant. Additionally, these strategies' other benefits are quick-to-match and the face image's strength [18][19].…”

Section: Model-based Methodsmentioning

confidence: 99%

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Technique of Feature Extraction from an image in Face Recognition with Implementation using own created dataset

Syed¹,

Syed²

2023

Preprint

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Face recognition is becoming a popular field in the evolution of computer applications in the last several years due to the vast range of software. Face identification is arduous because of the enormous variety of faces. Facial recognition is related to image processing, pattern recognition, and computer vision. The development of face recognition algorithms necessitates constantly creating new methods to face authentication technology. A review of human face recognition is mentioned in this paper. The Application and Principles of face recognition are discussed along with the face databases used to determine the efficiency of these face recognition algorithms and their flaws. Two basic techniques for feature extractions are analyzed to identify a face. Model-based methods, appearance-based and hybrid systems are described. The features of suitable face identification, distance measurements, face databases, classification, and an overview of necessary person recognition studies are also discussed with the implementation of the own created dataset. Finally, proposed techniques and challenges with future trends are highlighted.

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Section: Model-based Methodsmentioning

confidence: 99%

“…Although solving an eigenvalue problem requires KPCA, it does not require any extra optimization. Furthermore, there is no need to specify many significant components before the modeling [18]. This research [48] presents a new methodology for managing facial expressions and extracting appropriate features.…”

Section: Kernel Principle Component Analysis (Kpca)mentioning

confidence: 99%

Technique of Feature Extraction from an image in Face Recognition with Implementation using own created dataset

Syed¹,

Syed²

2023

Preprint

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“…We must note: The LBP procedure was expanded to use a different number of radius and neighbors, it is called Circular LBP [28].…”

Section: Fig 3 Lbph Example [27]mentioning

confidence: 99%

“…So the algorithm output is the ID from the image with the closest histogram. The algorithm should also return the calculated distance, which can be used as a 'confidence' measurement [28].…”

Section: Fig 4 Extracting Histogram [27]mentioning

confidence: 99%

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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“…It regards data as points in a high-dimensional space, operating by finding a new coordinate system for a dataset, with the axes (or principal components) ordered by the degree of variance contained within the training data. A set of faces can be represented as points in this new coordinate system(Kurmi, Agrawal, & Baghel, 2014). This method evaluates each one of pixels independently, means, it does not model relationships between neighboring image pixels.…”

mentioning

confidence: 99%

Discrete Wavelet Transform with Eigenface to Enhance Face Recognition Rate

2018

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In a recent past, face recognition was one of the most popular methods and successful application of image processing field which is widely used in security and biometric applications. The innovation of new approaches to face identification technologies is continuously subject to building much strong face recognition algorithms. Face recognition in real-time applications has been fast-growing challenging and interesting. The human face identification process is not trivial task especially different face lighting and poses are captured to be matched. In this study, the proposed method is tested using a benchmark ORL database that contains 400 images of 40 persons as the variant posse, lighting, etc. Discrete avelet Transform technique is applied on the ORL database to enhance the accuracy and the recognition rate. The best recognition rate result obtained is 99.25%, when tested using 9 training images and 1 testing image with cosine distance measurement. The recognition rate Increased when applying 2-level of DWT with the bior5.5 filter on training image database and the test image. For feature extraction and dimension reduction, PCA is used. Euclidean distance, Manhattan distance, and Cosine distance are Distance measures used for the matching process.

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Study of Different Face Recognition Algorithms and Challenges

Cited by 10 publications

References 12 publications

Technique of Feature Extraction from an image in Face Recognition with Implementation using own created dataset

Technique of Feature Extraction from an image in Face Recognition with Implementation using own created dataset

An Accurate System for Face Detection and Recognition

Discrete Wavelet Transform with Eigenface to Enhance Face Recognition Rate

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