A hybrid fuzzy framework for face detection and recognition using behavioral traits

Girdhar, Palak; Virmani, Deepali; Kumar, Sumit

doi:10.1080/09720510.2019.1580905

Cited by 7 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The experiments proved that our approach is more efficient than other currently proposed approaches. Hassan et al (2021) proposed a descriptor for face recognition called (QKrMs); Pan, Li & Zhu (2015) proposed (QALMs); Pan, Li & Zhu (2020) proposed another descriptor called (QCMs); and Sukhjeet et al proposed (QMV) approach for color face recognition ( Ranade & Anand, 2021 ); Vinay et al (2017) presented two methods for facial recognition based on convolution neural network called (FCNN & GCNN); Girdhar, Virmani & Kumar (2019) proposed a hybrid Fuzzy based Behavior Prognostic System for Disparate traits (FBPSDs) which uses face recognition to identify the contextual faces; Hosny, Abd Elaziz & Darwish (2021) proposed a novel color face recognition method that depends on a new family of fractional-order orthogonal functions called (FrMEMs); Virmani et al (2019) presented face detection and recognition approach based on deep learning called (FDREnet); Ayoub et al (2021) presented Deep learning models “Openface via PSO and introduced customized Inception-V3 model via PSO.…”

Section: Resultsmentioning

confidence: 99%

Fast and accurate face recognition system using MORSCMs-LBP on embedded circuits

Hosny

Hamad

Elkomy

et al. 2022

PeerJ Computer Science

View full text Add to dashboard Cite

Because of the current COVID-19 circumstances in the world and the tremendous technological developments, it has become necessary to use this technology to combat the spread of the new coronavirus. The systems that depend on using hands, such as fingerprint systems and PINs in ATM systems, could lead to infection, so they have become undesirable and we can replace them by using facial recognition instead. With the development of technology and the availability of nano devices like the Raspberry Pi, such applications can be implemented easily. This study presents an efficient face recognition system in which the face image is taken by a standalone camera and then passed to the Raspberry Pi to extract the face features and then compare them with the database. This approach is named MORSCMs-LBP by combining two algorithms for feature extraction: Local Binary Pattern (LBP) as a local feature descriptor and radial substituted Chebyshev moments (MORSCMs) as a global feature descriptor. The significant advantage of this method is that it combines the local and global features into a single feature vector from the detected faces. The proposed approach MORSCMs-LBP has been implemented on the Raspberry Pi 4 computer model B with 1 GB of RAM using C++ OpenCV. We assessed our method on various benchmark datasets: face95 with an accuracy of 99.0278%, face96 with an accuracy of 99.4375%, and grimace with 100% accuracy. We evaluated the proposed MORSCMs-LBP technique against other recently published approaches; the comparison shows a significant improvement in favour of the proposed approach.

show abstract