Face Recognition Comparative Analysis Using Different Machine Learning Approaches

• Autorzy: Ahmed Nisar , Khan Farhan Ajmal , Ullah Zain , Ahmed Hasnain , Shahzad Taimur , Ali Nableela

Identyfikatory

DOI

10.12913/22998624/132611

• Języki publikacji: EN

Abstrakty

EN

The problem of a facial biometrics system was discussed in this research, in which different classifiers were used within the framework of face recognition. Different similarity measures exist to solve the performance of facial recognition problems. Here, four machine learning approaches were considered, namely, K-nearest neighbor (KNN), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Principal Component Analysis (PCA). The usefulness of multiple classification systems was also seen and evaluated in terms of their ability to correctly classify a face. A combination of multiple algorithms such as PCA+1NN, LDA+1NN, PCA+ LDA+1NN, SVM, and SVM+PCA was used. All of them performed with exceptional values of above 90% but PCA+LDA+1N scored the highest average accuracy, i.e. 98%.

Słowa kluczowe

EN

linear discriminant analysis; k-nearest neighbor; support vector machine; principal component analysis

PL

liniowa analiza dyskryminacyjna; maszyna wektorów podporowych; analiza głównych składowych

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2021
• Tom: Vol. 15, no 1
• Strony: 265--272
• Opis fizyczny: Bibliogr. 22 poz., fig., tab.

Twórcy

autor

Ahmed Nisar

• Department of Computer Science, Sapienza University of Rome, Italy

autor

Khan Farhan Ajmal

• Department of Artificial Intelligence, Sapienza University of Rome, Italy

autor

Ullah Zain

• Department of Data Science, Sapienza University of Rome, Italy

autor

Ahmed Hasnain

• Department of Computer Science, Sapienza University of Rome, Italy

autor

Shahzad Taimur

• Department of Computer Science, COMSATS University Islamabad, Attock Campus, Pakistan

autor

Ali Nableela

• Department of Information Technology, Quaid-e-Awam University, Nawabshah, Pakistan

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).