Principle Component Analysis algorithm (PCA) for image recognition

Alkandari, Abdulrahman; Aljaber, Soha

doi:10.1109/icctim.2015.7224596

Cited by 27 publications

(10 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Huang and Yin [20] compare and investigate linear PCA and various nonlinear techniques for face recognition. Alkandari and Aljaber [21] have presented the importance of PCA to identify the facial image without human intervention [21]. Dandpat and Meher proposed a face recognition for improving performance using PCA and two-dimensional PCA in [22].…”

Section: Related Workmentioning

confidence: 99%

PCA-based dimensionality reduction for face recognition

et al. 2021

View full text Add to dashboard Cite

In this paper, we conduct a comprehensive study on dimensionality reduction (DR) techniques and discuss the mostly used statistical DR technique called principal component analysis (PCA) in detail with a view to addressing the classical face recognition problem. Therefore, we, more devotedly, propose a solution to either a typical face or individual face recognition based on the principal components, which are constructed using PCA on the face images. We simulate the proposed solution with several training and test sets of manually captured face images and also with the popular Olivetti Research Laboratory (ORL) and Yale face databases. The performance measure of the proposed face recognizer signifies its superiority.

show abstract

Section: Related Workmentioning

confidence: 99%

PCA-based dimensionality reduction for face recognition

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Whether how much these features can describe the dataset is of great importance. [10] In this paper, the PCA method is adopted to determine the importance and value of each feature for classifying patients.…”

Section: Principle Components Analysis (Pca)mentioning

confidence: 99%

Clustering of Infected Patients by COVID-19 Using Self-Organized Mapping and Extracting the Most Important Clinical Features

Heydari

Kahrobaee

YazdanPanah

et al. 2020

2020 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS)

View full text Add to dashboard Cite

With the advent of the Covid-19 virus in early 2020 and the worldwide spread of the disease, various attempts have been made to identify and classify patients infected by COVID-19. According to the latest information in the protocol provided by the ministry of Health and Medical Treatment of Iran, patients' statuses are divided into four phases. Each phase of the disease has its own characteristics. In this study, information about patients with Covid-19 are collected by physicians specialized in infectious diseases. Patients' characteristics are classified based on clinical symptoms, laboratory parameters, and radiological images.In the proposed method, patients are classified according to their features, without supervision, and labels. The obtained results are compared with physicians' diagnosis. The results have revealed that the accuracy of the self-organized mapping is 92.5%. In addition, after clustering, the most important features of clusters are extracted by using the PCA method. After analysis and classification, the most important common features which can distinguish considered patients from other patients are: age, lung involvement and SPO2. Based on these features, the initial screening of patients can be performed with a high reliability.

show abstract

“…Mathematically, the PCA formula is accumulated by the standard deviation, the eigenvectors, and the eigenvalues [10]. The classification process is used to estimate the difference between the database images and test images using distance methods like the typical Euclidean Distance method [11].…”

Section: Introductionmentioning

confidence: 99%

“…A variety of methods are implemented to improve image recognition and reduce the database. A. Alkandari, S. J. Aljaber, 2015 [11], PCA implemented for recognition, identification of facial expression without human intervention. PCA is used to reduce the size of the matrix for image recognition.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Clustering with a minimum Pattern of Features Extraction for Person Recognition

Safi

Abbas

lbrahim

2021

DJES

View full text Add to dashboard Cite

In personal image recognition algorithms, two effective factors govern the system’s evaluation, recognition rate and size of the database. Unfortunately, the recognition rate proportional to the increase in training sets. Consequently, that increases the processing time and memory limitation problems. This paper’s main goal was to present a robust algorithm with minimum data sets and a high recognition rate. Images for ten persons were chosen as a database, nine images for each individual as the full version of the training data set, and one image for each person out of the training set as a test pattern before the database reduction procedure. The proposed algorithm integrates Principal Component Analysis (PCA) as a feature extraction technique with the minimum means of clusters and Euclidean Distance to achieve personal recognition. After indexing the training set for each person, the clustering of the differences is determined. The recognition of the person represented by the minimum mean index; this process returned with each reduction. The experimental results show that the recognition rate is 100% despite reducing the training sets to 44%, while the recognition rate decrease to 70% when the reduction reaches 89%. The clear picture out is the results of the proposed system support the idea of the redaction of training sets in addition to obtaining a high recognition rate based on application requirements.

show abstract

Principle Component Analysis algorithm (PCA) for image recognition

Cited by 27 publications

References 2 publications

PCA-based dimensionality reduction for face recognition

PCA-based dimensionality reduction for face recognition

Clustering of Infected Patients by COVID-19 Using Self-Organized Mapping and Extracting the Most Important Clinical Features

The Effect of Clustering with a minimum Pattern of Features Extraction for Person Recognition

Contact Info

Product

Resources

About