Assessing the Performance of DWT-PCA/SVD Face Recognition Algorithm under Multiple Constraints
Open Access
- 20 September 2021
- journal article
- research article
- Published by Hindawi Limited in Journal of Applied Mathematics
- Vol. 2021, 1-12
- https://doi.org/10.1155/2021/7060270
Abstract
Many architectures of face recognition modules have been developed to tackle the challenges posed by varying environmental constraints such as illumination, occlusions, pose, and expressions. These recognition systems have mainly focused on a single constraint at a time and have achieved remarkable successes. However, the presence of multiple constraints may deteriorate the performance of these face recognition systems. In this study, we assessed the performance of Principal Component Analysis and Singular Value Decomposition using Discrete Wavelet Transform (DWT-PCA/SVD) for preprocessing face recognition algorithm on multiple constraints (partially occluded face images acquired with varying expressions). Numerical evaluation of the study algorithm gave reasonably average recognition rates of 77.31 and 76.85 for left and right reconstructed face images with varying expressions, respectively. A statistically significant difference was established between the average recognition distance of the left and right reconstructed face images acquired with varying expressions using pairwise comparison test. The post hoc analysis using the Bonferroni simultaneous confidence interval revealed that the significant difference established through the pairwise comparison test was mainly due to the sad expressions. Although the performance of the DWT-PCA/SVD algorithm declined as compared to its performance on single constraints, the algorithm attained appreciable performance level under multiple constraints. The DWT-PCA/SVD recognition algorithm performs reasonably well for recognition when partial occlusion with varying expressions is the underlying constraint.Keywords
This publication has 22 references indexed in Scilit:
- Face recognition using transform domain feature extraction and PSO-based feature selectionApplied Soft Computing, 2014
- Intra-Class Variation Reduction Using Training Expression Images for Sparse Representation Based Facial Expression RecognitionIEEE Transactions on Affective Computing, 2014
- Robust Kernel Representation With Statistical Local Features for Face RecognitionIEEE Transactions on Neural Networks and Learning Systems, 2013
- 3D Face Recognition under Expressions, Occlusions, and Pose VariationsIEEE Transactions on Pattern Analysis and Machine Intelligence, 2013
- A Brief Survey of Color Image Preprocessing and Segmentation TechniquesJournal of Pattern Recognition Research, 2011
- Resolution enhancement based on learning the sparse association of image patchesPattern Recognition Letters, 2010
- Why Is Facial Occlusion a Challenging Problem?Lecture Notes in Computer Science, 2009
- PCA versus LDAIEEE Transactions on Pattern Analysis and Machine Intelligence, 2001
- Vector directional filters-a new class of multichannel image processing filtersIEEE Transactions on Image Processing, 1993
- Eigenfaces for RecognitionJournal of Cognitive Neuroscience, 1991