@Article{cmc.2022.023052,
AUTHOR = {Anil Kumar Yadav, R. K. Pateriya, Nirmal Kumar Gupta, Punit Gupta, Dinesh Kumar Saini, Mohammad Alahmadi},
TITLE = {Hybrid Machine Learning Model for Face Recognition Using SVM},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {72},
YEAR = {2022},
NUMBER = {2},
PAGES = {2697--2712},
URL = {http://www.techscience.com/cmc/v72n2/47153},
ISSN = {1546-2226},
ABSTRACT = {Face recognition systems have enhanced human-computer interactions in the last ten years. However, the literature reveals that current techniques used for identifying or verifying faces are not immune to limitations. Principal Component Analysis-Support Vector Machine (PCA-SVM) and Principal Component Analysis-Artificial Neural Network (PCA-ANN) are among the relatively recent and powerful face analysis techniques. Compared to PCA-ANN, PCA-SVM has demonstrated generalization capabilities in many tasks, including the ability to recognize objects with small or large data samples. Apart from requiring a minimal number of parameters in face detection, PCA-SVM minimizes generalization errors and avoids overfitting problems better than PCA-ANN. PCA-SVM, however, is ineffective and inefficient in detecting human faces in cases in which there is poor lighting, long hair, or items covering the subject's face. This study proposes a novel PCA-SVM-based model to overcome the recognition problem of PCA-ANN and enhance face detection. The experimental results indicate that the proposed model provides a better face recognition outcome than PCA-SVM.},
DOI = {10.32604/cmc.2022.023052}
}