Hybrid Machine Learning Model for Face Recognition Using SVM

Face recognition systems have enhanced human-computer interac-tions in the last ten years. However, the literature reveals that current tech-niques 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 inef-ficient 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.

Automated summary

Face recognition systems have improved human-computer interactions, with PCA-SVM showing better generalization capabilities and avoiding overfitting compared to PCA-ANN. However, PCA-SVM is ineffective in cases of poor lighting, long hair, or items covering the face. This study proposes a novel PCA-SVM-based model to overcome PCA-ANN's recognition issues and enhance face detection.