Hybrid Machine Learning Model for Face Recognition Using SVM

• Published in: Computers, materials & continua Vol. 72; no. 2; pp. 2697 - 2712
• Main Authors: Kumar Yadav, Anil, K. Pateriya, R., Kumar Gupta, Nirmal, Gupta, Punit, Kumar Saini, Dinesh, Alahmadi, Mohammad
• Format: Journal Article
• Language: English
• Published: Henderson Tech Science Press 2022
• Subjects: Artificial neural networks; Face recognition; Learning theory; Machine learning; Object recognition; Principal components analysis; Support vector machines
• DOI: 10.32604/cmc.2022.023052
• ISSN: 1546-2226; 1546-2218

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.