Color Face Recognition by Using Quaternion and Deep Neural Networks

Alami, Abdelmajid El; Lakhili, Zouhir; Mesbah, Abderrahim; Berrahou, Aissam; Qjidaa, Hassan

doi:10.1109/wits.2019.8723788

Cited by 7 publications

(1 citation statement)

References 15 publications

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“…Orthogonal moments proved its efficiency in various pattern recognition applications such as color image watermark ( Darwish, Hosny & Kamal, 2020 ), Ranade & Anand (2021) proposed color face recognition technique based on Zernike quaternion moment vector and using quaternion vector moment (QVM) similarity distance to enhance accuracy, and the experiment proved its superiority on all other techniques. Abdelmajid et al introduced a face recognition system based on quaternion moment and deep neural network (DNN), it is computationally low cost and also accurate ( Alami et al, 2019 ). Hosny (2019) proposed another method for face recognition using exact Gaussian-hermit moments and it could overcome geometric distortions.…”

Section: Introductionmentioning

confidence: 99%

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

Hosny

Hamad

Elkomy

et al. 2022

PeerJ Computer Science

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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.

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