Locally Linear Embedding of Local Orthogonal Least Squares Images for Face Recognition

International Conference on Applied Electronic and Engineering 2017

无线电电子学;工业技术

Kamaru Zaman, Fadhlan Hafizhelmi^1

Faculty of Electrical Engineering, Universiti Teknologi, MARA, Selangor Shah Alam

40450, Malaysia^1

关键词: Convolutional Neural Networks (CNN);    Dimensionality reduction;    Discriminant informations;    Feature extraction methods;    Global coordinate systems;    Least squares regression;    Locally linear embedding;    Orthogonal least squares;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/341/1/012026/pdf
DOI  :  10.1088/1757-899X/341/1/012026

学科分类：工业工程学

来源: IOP

【 摘 要 】

Dimensionality reduction is very important in face recognition since it ensures that high-dimensionality data can be mapped to lower dimensional space without losing salient and integral facial information. Locally Linear Embedding (LLE) has been previously used to serve this purpose, however, the process of acquiring LLE features requires high computation and resources. To overcome this limitation, we propose a locally-applied Local Orthogonal Least Squares (LOLS) model can be used as initial feature extraction before the application of LLE. By construction of least squares regression under orthogonal constraints we can preserve more discriminant information in the local subspace of facial features while reducing the overall features into a more compact form that we called LOLS images. LLE can then be applied on the LOLS images to maps its representation into a global coordinate system of much lower dimensionality. Several experiments carried out using publicly available face datasets such as AR, ORL, YaleB, and FERET under Single Sample Per Person (SSPP) constraint demonstrates that our proposed method can reduce the time required to compute LLE features while delivering better accuracy when compared to when either LLE or OLS alone is used. Comparison against several other feature extraction methods and more recent feature-learning method such as state-of-the-art Convolutional Neural Networks (CNN) also reveal the superiority of the proposed method under SSPP constraint.