Additive Parameter for Deep Face Recognition

Article Properties
Cite
Ul Rahman, Jamshaid, et al. “Additive Parameter for Deep Face Recognition”. Communications in Mathematics and Statistics, vol. 8, no. 2, 2019, pp. 203-17, https://doi.org/10.1007/s40304-019-00198-z.
Ul Rahman, J., Chen, Q., & Yang, Z. (2019). Additive Parameter for Deep Face Recognition. Communications in Mathematics and Statistics, 8(2), 203-217. https://doi.org/10.1007/s40304-019-00198-z
Ul Rahman, Jamshaid, Qing Chen, and Zhouwang Yang. “Additive Parameter for Deep Face Recognition”. Communications in Mathematics and Statistics 8, no. 2 (2019): 203-17. https://doi.org/10.1007/s40304-019-00198-z.
Ul Rahman J, Chen Q, Yang Z. Additive Parameter for Deep Face Recognition. Communications in Mathematics and Statistics. 2019;8(2):203-17.
Journal Category
Science
Mathematics
Refrences
Title Journal Journal Categories Citations Publication Date
10.1109/MSP.2017.2764116 2018
10.1109/LSP.2018.2822810 2018
10.1109/LSP.2016.2603342 2016
10.1007/978-3-319-46478-7_31 2016
10.1007/978-3-319-24261-3_7 2015
Citations
Title Journal Journal Categories Citations Publication Date
Ghost-Unet: multi-stage network for image deblurring via lightweight subnet learning The Visual Computer
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics: Computer engineering. Computer hardware
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
 2024
Mathematical modeling and simulation of biophysics systems using neural network

 International Journal of Modern Physics B
  • Science: Physics
  • Science: Physics
  • Science: Mathematics
  • Technology: Chemical technology
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Materials of engineering and construction. Mechanics of materials
  • Science: Physics
 2023
Performance analysis of convolutional neural networks for image classification with appropriate optimizers

 i-manager’s Journal on Mathematics 2023
An Inertial Extragradient Direction Method with Self-Adaptive Step Size for Solving Split Minimization Problems and Its Applications to Compressed Sensing

 Mathematics
  • Science: Mathematics
  • Science: Mathematics
 2022
Multi-Block Color-Binarized Statistical Images for Single-Sample Face Recognition

 Sensors
  • Technology: Chemical technology
  • Science: Chemistry: Analytical chemistry
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks
  • Science: Mathematics: Instruments and machines
  • Science: Chemistry: Analytical chemistry
  • Science: Chemistry
 39 2021
Citations Analysis
Category Category Repetition
Science: Mathematics2
Technology: Chemical technology2
Science: Physics1
Technology: Electrical engineering. Electronics. Nuclear engineering: Materials of engineering and construction. Mechanics of materials1
Science: Chemistry: Analytical chemistry1
Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks1
Science: Mathematics: Instruments and machines1
Science: Chemistry1
Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software1
Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics: Computer engineering. Computer hardware1
Science: Mathematics: Instruments and machines: Electronic computers. Computer science1
The category Science: Mathematics 2 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Multi-Block Color-Binarized Statistical Images for Single-Sample Face Recognition and was published in 2021. The most recent citation comes from a 2024 study titled Ghost-Unet: multi-stage network for image deblurring via lightweight subnet learning. This article reached its peak citation in 2023, with 2 citations. It has been cited in 5 different journals, 40% of which are open access. Among related journals, the The Visual Computer cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.
Citations used this article by year