Performance Improvement of Face Recognition Method and Application for the COVID-19 Pandemic
Suparna Biswas · Acta Polytechnica Hungarica · 2022
In this paper, a novel framework is introduced by combining compressive sensing(CS) theory, digital curvelet transform, and Principal Component Analysis to improve the performance of face recognition method.CS is a highly attractive approach in the field of signal processing, which provides an efficient way of data sampling at a lower rate than the Nyquist sampling rate.CS offers numerous advantages, like less memory storage, less power consumption and higher data transmission rate etc.Here, CS is used on the face images, which offers reduction in storage space and computational time.The use of curvelet transform provides dual benefits: (i) sparse representation (ii) improvement on detailed content.To extract the feature vector, the Principal Component Analysis is then applied.The Performance of the proposed face recognition method is computed by applying cross-validation technique, compressive sensing based classifier, neural network, Naive Bayes and Support Vector Machine classifier.The proposed technique can efficiently perform the face recognition, at a low computational cost.Extensive experiments, on ORL and AR face databases, are conducted to validate our claim.The proposed technique also recognizes face images more efficiently than the traditional PCA, with a 1.5% higher recognition rate, if a person wears a face mask, as protection from