A face recognition software framework based on principal component analysis

Peng, Peng; Portugal, Ivens; Alencar, Paulo; Cowan, Donald

doi:10.1371/journal.pone.0254965

Cited by 16 publications

(6 citation statements)

References 65 publications

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“…During the conversion, most representative data is kept and the noisy, redundant data is removed. Because of this feature, PCA is extensively applied as a dimensionality reduction tool in face recognition systems [24].In this paper, the optimal number of features required for each feature extraction method to get the highest accuracy is discussed.…”

Section: Related Workmentioning

confidence: 99%

Untitled

2023

IJAIA

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Facial recognition (FR) is a pattern recognition problem, in which images can be considered as a matrix of pixels.There are manychallenges that affect the performance of face recognitionincluding illumination variation, occlusion, and blurring. In this paper,a few preprocessing techniques are suggested to handle the illumination variationsproblem. Also, other phases of face recognition problems like feature extraction and classification are discussed. Preprocessing techniques like Histogram Equalization (HE), Gamma Intensity Correction (GIC), and Regional Histogram Equalization (RHE) are tested inthe AT&T database. For feature extraction, methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Independent Component Analysis (ICA), and Local Binary Pattern (LBP) are applied. Support Vector Machine (SVM) is used as the classifier. Both holistic and block-based methods are tested using the AT&T database. For twelve different combinations of preprocessing, feature extraction, and classification methods, experiments involving various block sizes are conducted to assess the computation performance and recognition accuracy for the AT&T dataset.Using the block-based method, 100% accuracy is achieved with the combination of GIC preprocessing, LDA feature extraction,and SVM classification using 2x2 block-sizingwhile the holistic method yields the maximum accuracy of 93.5%. The block-sized algorithm performs better than the holistic approach under poor lighting conditions.SVM Radial Basis Function performs extremely well on theAT&Tdataset for both holistic and block-based approaches.

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Section: Related Workmentioning

confidence: 99%

Untitled

2023

IJAIA

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“…It is a technique that automatically recognizes individuals by analyzing their unique facial features, such as the shape of the eyes, nose, mouth, and other attributes. Facial recognition technology is constantly evolving and improving, resulting in higher accuracy and enabling new possibilities for its use in different aspects of life [1].…”

Section: Introductionmentioning

confidence: 99%

Face recognition using ten-variate prediction ellipsoidsfor normalized datawith different quantiles

Prykhodko,

Trukhov

2024

AAIT

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Facial recognition technology plays a pivotal role in various domains, including security systems, entertainment, and identity ver-ification. However, the low probability of identifying a person by face can have negative consequences, highlighting the need forthe development and improvement of face recognition methods. The object of research is the face recognition process, with the subject of the research being a mathematical model for face recognition.One common approach in pattern recognition is usingdecision rules based onprediction ellipsoid. A significant challenge in its application is ensuring that the data conforms to a multivariate normal distribution. However, real-world data often doesn't adhere to this assumption, leading to reduced recognition probability. Therefore, there's a necessity to enhance mathematical models to accommodate such deviations.Another factor that can impact the outcome is the selection of different distribution quantiles, such as those from the Chi-square and F-distribution.For large datasets, the utilization of Chi-square and F-distribution in prediction ellipsoids typically results in similar probabilities, but there are data for which this is not the case and the application of predictionellipsoids with different quantiles of the distributions gives different results.This study investi-gates theapplicationof prediction ellipsoids in facial recognition tasks using different normalization techniques and distribution quan-tiles. The purpose of the work is to improve the probability of face recognition by building a ten-variate prediction ellipsoid for nor-malized data with different quantiles of distributions. We conducted experiments on a dataset of facial images and constructed predic-tion ellipsoids based on the Chi-square and F-distribution, utilizing both univariate and multivariate normalization techniques.Our findings reveal that normalization techniques significantly enhance recognition accuracy, with multivariate methods, such as the ten-variate Box-Cox transformation, outperforming univariate approaches. Furthermore, prediction ellipsoids constructed using the Chi-square distribution quantile generally exhibit superior performance compared to those constructed using the F-distribution quantile. Future investigations could explore the efficacy of alternative normalization techniques, such as the Johnson transformation, and ana-lyze the construction of prediction ellipsoids with alternative components of the ellipsoid equation.

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“…In this article, we examine the effects of dimensionality reduction on the effectiveness or precision of facial recognition algorithms using machine learning. The research is conducted using a variety of algorithms, including K-Nearest Neighbor, Support Vector Machine, Linear Regression, and Logistic Regression [1][2][3][4][5]. Based on the experiment's methodology, Logistic Regression works better in terms of accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Gu, Fuqiang, et al described about how to use a basic linear algebra approach like principal component analysis to create a crude face recognition system in [3]. Also, in anther work [4] helped us to understand about development of face recognition field, more advanced techniques are proposed, which might outperform the algorithms that we have already included in the framework.…”

Section: Introductionmentioning

confidence: 99%

Facial Image Expression Recognition and Prediction System

Talukder¹,

Ghosh

2023

Preprint

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Facial expression recognition system is an advanced technology that allows machines to recognize human emotions based on their facial expressions. We will use a dataset of human picture facial images in this research, which has more than 35500 facial photographs and represents seven different types of facial expression. To create our prediction model, we will use three different architecture models to create a system for predicting face expressions in people. We will analyze our data and make every effort to remove as much noise as we can before feeding that information to our model. We use the confusion matrix to assess the model's performance after it has been implemented effectively. To demonstrate the effectiveness of our model architecture, we will generate bar graphs and scatter plots for each model to display model loss and accuracy. The output of this model are visualize with actual class and predictive class and the result have graphical representation for each and every output facial Images which makes our recognition system user friendly.

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A face recognition software framework based on principal component analysis

Cited by 16 publications

References 65 publications

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Face recognition using ten-variate prediction ellipsoidsfor normalized datawith different quantiles

Facial Image Expression Recognition and Prediction System

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