TY  - EJOU
AU  - Yadav, Anil Kumar 
AU  - Pateriya, R. K. 
AU  - Gupta, Nirmal Kumar 
AU  - Gupta, Punit 
AU  - Saini, Dinesh Kumar 
AU  - Alahmadi, Mohammad 

TI  - Hybrid Machine Learning Model for Face Recognition Using SVM
T2  - Computers, Materials \& Continua

PY  - 2022
VL  - 72
IS  - 2
SN  - 1546-2226

AB  - 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.
KW  - Face recognition system (FRS); face identification; SVM; discrete cosine transform (DCT); artificial neural network (ANN); machine learning

DO  - 10.32604/cmc.2022.023052