期刊
IET COMPUTER VISION
卷 14, 期 4, 页码 122-130出版社
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-cvi.2019.0531
关键词
face recognition; feature extraction; learning (artificial intelligence); image classification; Gabor filters; normalised Gabor features; local variations; projected features; multiscale orientation; practical face recognition; relatively lengthy classification process; extensive local classifier; local cosine similarity classifier; manifold learning method; locally linear embedding; locally lateral normalised local Gabor feature vector; publicly available face datasets; feature compression; LGFV features; locally lateral manifolds
资金
- Universiti Teknologi MARA Shah Alam under Dana Kecemerlangan Bestari [600-IRMI/DANA 5/3/BESTARI (055/2017)]
Due to inherent characteristics of multiscale and orientation, normalised Gabor features have been successfully used in face recognition. Various previous works have showcased the strength and feasibility of this approach, especially on its robustness against local variations. However, the projected features are numerous and substantial in dimension, which is largely due to the convolution of multiscale and orientation of wavelets. Such features, when used in practical face recognition, would require relatively lengthy classification process, particularly when it involves computationally extensive local classifier or experts, such as ensembles of local cosine similarity (ELCS) classifier. The authors address this issue by simultaneously reducing the size of Gabor features laterally and locally using a manifold learning method called locally linear embedding (LLE). This method is thus denoted as locally lateral normalised local Gabor feature vector with LLE (LGFV/LN/LLE). Results on several publicly available face datasets reveal the superiority of the authors' approach in terms of improvements in feature compression of LGFV features by up to a reduction of 95% of total dimensionality while increasing the average classification accuracy by 26%. Altogether, the authors show that their LGFV/LN/LLE augmented by ELCS classifiers delivers equivalent result when compared against the state-of-the-art.
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