Face recognition algorithm based on stack denoising and self-encoding LBP

Autor: Lu Yanjing, Khan Mudassir, Ansari Mohd Dilshad
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Journal of Intelligent Systems, Vol 31, Iss 1, Pp 501-510 (2022)
Druh dokumentu: article
ISSN: 2191-026X
DOI: 10.1515/jisys-2022-0011
Popis: To optimize the weak robustness of traditional face recognition algorithms, the classification accuracy rate is not high, the operation speed is slower, so a face recognition algorithm based on local binary pattern (LBP) and stacked autoencoder (AE) is proposed. The advantage of LBP texture structure feature of the face image as the initial feature of sparse autoencoder (SAE) learning, use the unified mode LBP operator to extract the histogram of the blocked face image, connect to form the LBP features of the entire image. It is used as input of the stacked AE, feature extraction is done, realize the recognition and classification of face images. Experimental results show that the recognition rate of the algorithm LBP-SAE on the Yale database has achieved 99.05%, and it further shows that the algorithm has a higher recognition rate than the classic face recognition algorithm; it has strong robustness to light changes. Experimental results on the Olivetti Research Laboratory library shows that the developed method is more robust to light changes and has better recognition effects compared to traditional face recognition algorithms and standard stack AEs.
Databáze: Directory of Open Access Journals