The techniques for face recognition with support vector machines

Frolov, Igor; Sadykhov, Rauf

doi:10.1109/imcsit.2009.5352696

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…The Indian government has started a computerized locker framework for everyone putting away every single private certification on the administration's server. The proposition visa in 10 days, month-to-month benefits and provident trust are likewise all the while [37][38][39]. The system for counting people on public transport was discussed in 2018 by Patricia Chato and Nancy Velasco.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Machine Learning Model for Face Recognition Using SVM

Yadav¹,

Pateriya²,

Gupta³

et al. 2022

Computers, Materials &Amp; Continua

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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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Section: Introductionmentioning

confidence: 99%