e-ISSN: 2582-5208
International Research Journal of Modernization in Engineering Technology and Science ( Peer-Reviewed, Open Access, Fully Refereed International Journal ) Volume:05/Issue:05/May-2023 Impact Factor- 7.868 www.irjmets.com
FACE RECOGNITION ALGORITHMS: A COMPARATIVE STUDY Himanshu Dorbi*1, Prabhakar Joshi*2 *1,2Department of Computer Science & Engineering Graphic Era Hill University, Dehradun Uttarakhand, India
DOI : https://www.doi.org/10.56726/IRJMETS41255
ABSTRACT This research paper analyses Eigenfaces, Fisherfaces, KLT (Kanade-Lucas-Tomasi) and Viola-Jones face recognition algorithms. Examined under various conditions, including lighting changes, design changes, nonsolid deformations, occlusions, anomalies in the dataset, and image variations and resolution, the study examines its limitations, strengths and functions. The truth, speaking, is to give insight into their comparisons. Useful tips for choosing an algorithm based on the situation. Eigenfaces and Fisherfaces perform well in a controlled environment with limited variability. Viola-Jones has demonstrated high accuracy in face detection and object detection. KLT is mainly used for feature monitoring and optical measurement. This outcome facilitates a comprehensive understanding of the strengths and limitations of each algorithm, helping to make informed decisions about facial recognition in a variety of situations.
I.
INTRODUCTION
Face recognition Facial recognition algorithms play an important role in many applications, from security systems to human-computer interaction. This research paper presents an analysis of four face recognition systems: Eigenfaces, Fisherfaces, KLT (Kanade-Lucas-Tomasi) and Viola-Jones. The aim is to evaluate their limitations, strengths and performances in different situations, including lighting changes, lighting changes, non-hard deformations, occlusions, dataset aberrations, image quality and resolution. Based on this analysis, this study aims to offer suggestions for the comparison of algorithms. It examines the accuracy achieved by each algorithm and highlights its advantages and disadvantages. In addition, recommendations will be made to guide the selection algorithm on the basis of specific conditions and requirements. Eigenfaces and herringbones based on PCA and FLDA techniques, respectively, are particularly useful in control environments with limited variability. Known for its high accuracy in face detection and object recognition, Viola-Jones stands out in situations that require powerful detection capabilities. On the other hand, KLT is mainly used for feature tracking and visual evaluation, not face recognition. Understanding the unique properties of each algorithm and its performance in different situations is important for making informed decisions in facial recognition. Using these studies, doctors can choose the most appropriate algorithm that will be accurate and effective in their case.
II.
METHODOLOGY
Some of the commonly used face recognizing algorithm are as follows: Eigenfaces- Eigenfaces is a popular face recognition algorithm that was introduced by Matthew Turk and Alex Pentland in 1991. It revolutionized the field of face recognition by employing the concept of principal component analysis (PCA) for dimensionality reduction. The principle behind the Eigenfaces algorithm is to represent faces as a linear combination of eigenfaces, which are the principal components obtained through PCA. PCA is a statistical technique that aims to capture the most significant variations in a dataset by projecting it onto a lower-dimensional space.Maintaining the Integrity of the Specifications. The Eigenfaces algorithm follows a series of steps to perform face recognition: ●
Data Collection: Collect a dataset of face images representing different individuals under various conditions.
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Preprocessing: Enhance face images through grayscale conversion, histogram equalization, and geometric normalization.
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Dimensionality Reduction: Apply PCA to reduce the dimensionality of preprocessed face images, extracting eigenfaces as principal components.
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