过度拟合
主成分分析
人工智能
支持向量机
模式识别(心理学)
计算机科学
面部识别系统
面子(社会学概念)
机器学习
人工神经网络
一般化
数学
数学分析
社会科学
社会学
作者
Anil Kumar Yadav,R. K. Pateriya,Nirmal Kumar Gupta,Punit Gupta,Dinesh Kumar Saini,Mohammad Alahmadi
标识
DOI:10.32604/cmc.2022.023052
摘要
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.
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