Face Detection Using Eigenfaces: A Comprehensive Review

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 118406 - 118426

Published: Jan. 1, 2024

This paper thoroughly reviews face detection techniques, primarily focusing on applying Eigenfaces, a powerful method rooted in Principal Component Analysis (PCA). The goal is to provide comprehensive understanding of the advancements, challenges, and prospects associated with Eigenface-based systems. review commences exploring facial recognition system framework using Eigenfaces studying intricacies employing as foundational element for robust recognition. Then, we describe taxonomies various approaches systematic diverse strategies utilized Besides, explores benchmarking datasets tailored specifically These are critically analyzed, highlighting their relevance, limitations, potential impact developing assessing algorithms. Furthermore, details limitations open issues inherent Addressing concerns such sensitivity lighting conditions, occlusions, scalability, this section aims guide future research directions by identifying gaps current proposing avenues improvement.

Language: Английский

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Are Brain–Computer Interfaces Feasible With Integrated Photonic Chips?

Frontiers in Neuroscience, Journal Year: 2022, Volume and Issue: 15

Published: Jan. 7, 2022

The present paper examines the viability of a radically novel idea for brain-computer interface (BCI), which could lead to technological, experimental, and clinical applications. BCIs are computer-based systems that enable either one-way or two-way communication between living brain an external machine. read-out signals transduce them into task commands, performed by In closed loop, machine can stimulate with appropriate signals. recent years, it has been shown there is some ultraweak light emission from neurons within close visible near-infrared parts optical spectrum. Such photon (UPE) reflects cellular (and body) oxidative status, compelling pieces evidence beginning emerge UPE may well play informational role in neuronal functions. fact, several experiments point direct correlation intensity neural activity, reactions, EEG cerebral blood flow, energy metabolism, release glutamate. Therefore, we propose skull implant BCI uses UPE. We suggest photonic integrated chip installed on interior surface new form extraction relevant features current technology landscape, technologies advancing rapidly poised overtake many electrical technologies, due their unique advantages, such as miniaturization, high speed, low thermal effects, large integration capacity allow yield, volume manufacturing, lower cost. For our proposed BCI, making very major conjectures, need be experimentally verified, therefore discuss controversial parts, feasibility limitations, potential impact this envisaged if successfully implemented future.

Language: Английский

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Lessons from Twenty Years of Quantum Image Processing

ACM Transactions on Quantum Computing, Journal Year: 2024, Volume and Issue: unknown

Published: May 2, 2024

Quantum image processing (QIMP) was first introduced in 2003, by Venegas-Andraca et al. at the University of Oxford. This field attempts to overcome limitations classical computers and potentially overwhelming complexity algorithms providing a more effective way store manipulate visual information. Over past 20 years, QIMP has become an active area research, experiencing rapid vigorous development. However, these advancements have suffered from imbalance, as inherent critical issues been largely ignored. In this paper, we review original intentions for analyze various unresolved new perspective, including algorithm design, potential advantages limitations, technological debates, directions future We suggest 20-year milestone could serve beginning advocate researchers focus their attention on pursuit, helping bottlenecks, achieving practical results future.

Language: Английский

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Quantum enhanced probing of multilayered samples

Physical Review Research, Journal Year: 2023, Volume and Issue: 5(2)

Published: June 16, 2023

Quantum sensing exploits quantum phenomena to enhance the detection and estimation of classical parameters physical systems biological entities, particularly so as overcome inefficiencies its counterparts. A promising approach within is optical coherence tomography which relies on nonclassical light sources reconstruct internal structure multilayered materials. Compared traditional probing, provides enhanced-resolution images unaffected by even-order dispersion. One main limitations this technique lies in appearance artifacts echoes, i.e., fake structures that appear coincidence interferogram, hinder retrieval information required for scans. Here, utilizing a full theoretical model, combination with fast genetic algorithm postprocess data, we successfully extract morphology complex samples thoroughly distinguish real interfaces, artifacts, echoes. We test effectiveness model comparing predictions experimentally generated interferograms through controlled variation pump wavelength. Our results could potentially lead development practical high-resolution probing noninvasive scanning photodegradable materials biomedical imaging/sensing, clinical applications, science.

Language: Английский

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High-contrast interaction-free quantum imaging method

Physical review. A/Physical review, A, Journal Year: 2023, Volume and Issue: 107(3)

Published: March 17, 2023

Quantum imaging techniques offer enhanced resolution, contrast, and precision at ultralow illumination levels compared to traditional approaches. Relying on the unique properties of entangled photon pairs, two these stand out: correlation-based quantum technique provides visibility enhancement in a low-reflectivity object which is subject excessive noise losses, while interaction-free ghost allows for probing presence an with ultimately low number photons. Here we propose scheme that combines advantages We show this offers high-contrast objects minimal photons can minimize thermal efficiently create background-free images. anticipate approach find application photosensitive biological tissues noninvasive harm-free fashion.

Language: Английский

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Utilising Dimensionality Reduction for Improved Data Analysis with Quantum Feature Learning

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: April 9, 2024

Abstract This research explores the potential of quantum computing in data analysis, focusing on efficient analysis high-dimensional datasets using dimensionality reduction techniques. The study aims to fill knowledge gap by developing robust techniques that can mitigate noise and errors. methodology involved a comprehensive review existing techniques, such as principal component linear discriminant generative models. also explored limitations imposed NISQ devices proposed strategies adapt these work efficiently within constraints. key results demonstrate effectively reduce while preserving critical information. evaluation models showed their effectiveness improving particularly simulation speed predicting properties. Despite challenges posed errors, methods promise mitigating effects Finally, this contributes advancement presenting applications. It highlights importance feature learning operate noisy environments, especially era.

Language: Английский

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Analysis of Machine and Deep Learning Algorithms for Pattern Recognition in Medical Data

Published: Feb. 23, 2024

Pattern recognition is a data analysis technique that utilizes various algorithms for the automatic of patterns and regularities. Recently, problems scenes need pattern quick resolution difficult issues, particularly those can't resolved by multiple dimensional data, because involved in spectral information. In this study, different machine learning (ML) deep (DL) techniques are analyzed which implemented using medical data. This study discussed significant assumptions, advantages, drawbacks ML DL techniques. Different Artificial Neural Networks (ANN), Machine Learning Regression (MLR), so on. Various Convolutional (CNN), EfficientNet performance measures like accuracy, precision, recall, f1-score error rates used previous studies evaluation study. The concludes have potential to overcome every drawback there option integrating method developing an ensemble technique.

Language: Английский

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Face Expression Recognition: A Survey on Hyperparameter Optimization

Lecture notes in electrical engineering, Journal Year: 2024, Volume and Issue: unknown, P. 141 - 157

Published: Jan. 1, 2024

Language: Английский

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Dimensionality Reduction for Data Analysis With Quantum Feature Learning

Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 20, 2024

ABSTRACT To improve data analysis and feature learning, this study compares the effectiveness of quantum dimensionality reduction (qDR) techniques to classical ones. In study, we investigate several qDR on a variety datasets such as Gaussian distribution adaptation (qGDA), principal component (qPCA), linear discriminant (qLDA), t‐SNE (qt‐SNE). The Olivetti Faces, Wine, Breast Cancer, Digits, Iris are among used in investigation. Through comparison evaluations against well‐established approaches, PCA (cPCA), LDA (cLDA), GDA (cGDA), using metrics like loss, fidelity, processing time, these is assessed. findings show that cPCA produced positive results with lowest loss highest fidelity when dataset. On other hand, uniform manifold approximation projection (qUMAP) performs well shows strong tested Wine dataset, but ct‐SNE mediocre performance Digits Isomap locally embedding (LLE) function differently depending Notably, LLE showed largest Faces hypothesis testing strategies did not significantly outperform terms maintaining pertinent information from datasets. More specifically, outcomes paired t ‐tests it comes ability capture complex patterns, there no statistically significant differences between qPCA, cLDA qLDA, cGDA qGDA. According assessments mutual (MI) clustering accuracy, qPCA may be able recognize patterns more clearly than standardized cPCA. Nevertheless, discernible improvement qLDA qGDA approaches their counterparts.

Language: Английский

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A Discriminant Face Recognition Algorithm Based on Improved Softmax Loss Algorithm

Published: Oct. 21, 2023

Traditional Softmax loss algorithm has only separability for features algorithm. This study proposed an improved to recognize facial features. The first applies intra class cosine similarity between the and weight vectors based on feature distribution, making more compact separating classes as much possible; then, basis of loss, we use normalized better simulate low-quality images, reduce category imbalance by normalizing weights ensure consistency with measurement during testing; finally, joint normalization were fine-tuned pre trained model. achieved recognition rates >98% >93% face benchmark test sets LFW (labeled faces in wild) YTF (YouTube database), respectively. experimental results showed that large-scale recognition, discriminability features, Enhanced generalization ability model can effectively improve rate.

Language: Английский

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