Analysis on Texture Feature Extraction Methods for Face Recognition in New Born

Rahamathunnisa, U.; Sudhakar, K.

doi:10.1109/rteict52294.2021.9573777

Cited by 2 publications

(1 citation statement)

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“…Rahamathunnisa and Sudhakar [21] analyzed LBP with other methods (PCA and GLCM) used to extract features from the face image. The first method generates LBP descriptions, while PCA generates eigenvectors and eigenvalues, in addition to the GLCM method, which produces second-order statistical features.…”

Section: Introductionmentioning

confidence: 99%

Evaluating face recognition with different texture descriptions and convolution neural network

Al-Hameed

Mohammed

2023

IJEECS

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Extracting the remarkable attributes of the image objects is an issue of ongoing research special in the face recognition problem. This paper presents two directions. The first is a comparison between the local binary patterns (LBP) and its modified center symmetric LBP drawn from localized facial expressions and due to the efficiency, K-nearest neighbor (KNN) and the support vector machine (SVM) techniques play significant roles in this research used to implement the proposed system efficiently. The second direction proposes an efficient architecture by depending on deep learning convolution neural network (CNN) to implement face recognition. Such a design consists of two parts: a convolutional learning feature model and a classification model. The first one learns the important feature,while the second part produces a score class for each sample input. Many experiments are implemented on the known dataset once for the number of nearest neighbors (K value), and then decrease the number of expression samples for each individual the other time. The cross-validation method is used to provide a true picture of the accuracy of the face recognition system. In all experiment results, the center symmetric LBP with KNN outperforms the classic LBP. While significant progress in the results accuracy recognition ratio of the CNN model compared with other methods used.

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Section: Introductionmentioning

confidence: 99%