Nam, Gi Pyo Choi, Heeseung Cho, Junghyun Kim, Ig-Jae 2024-01-19T22:01:30Z 2024-01-19T22:01:30Z 2021-09-03 2018-09 2076-3417 https://pubs.kist.re.kr/handle/201004/120965 Face recognition is one research area that has benefited from the recent popularity of deep learning, namely the convolutional neural network (CNN) model. Nevertheless, the recognition performance is still compromised by the model's dependency on the scale of input images and the limited number of feature maps in each layer of the network. To circumvent these issues, we propose PSI-CNN, a generic pyramid-based scale-invariant CNN architecture which additionally extracts untrained feature maps across multiple image resolutions, thereby allowing the network to learn scale-independent information and improving the recognition performance on low resolution images. Experimental results on the LFW dataset and our own CCTV database show PSI-CNN consistently outperforming the widely-adopted VGG face model in terms of face matching accuracy. English MDPI EIGENFACES PSI-CNN: A Pyramid-Based Scale-Invariant CNN Architecture for Face Recognition Robust to Various Image Resolutions Article 10.3390/app8091561 1 APPLIED SCIENCES-BASEL, v.8, no.9 APPLIED SCIENCES-BASEL 8 9 scie scopus 000445760200150 2-s2.0-85053038169 Chemistry, Multidisciplinary Engineering, Multidisciplinary Materials Science, Multidisciplinary Physics, Applied Chemistry Engineering Materials Science Physics Article EIGENFACES face recognition deep learning pyramid-based approach scale-invariant low-resolution