A finger print recognition using CNN Model
- Title
- A finger print recognition using CNN Model
- Creator
- Minocha S.; Krishnachalitha K.C.; Gupta Chancellor S.; Alatba S.R.; Pund S.S.; Alfurhood B.S.
- Description
- The fundamental goal of this research is to improve the new identification accuracy for fingerprint acknowledgment by contrasting Convolutional Neural Networks (CNN) model frameworks for biometric safety in the cloud with Conventional inception models (TIM). Accuracy was computed and compared using a CNN model and standard Inception Models (N=10). The statistical significance was calculated using SPSS. Average and standard deviation for a 95% confidence interval, 0.05% G-power cutoff. The TIM and Convolutional Neural Networks performed an autonomous T-Test on the samples. CNN is more successful (93%) than TIM (61%). Based on a significant value of 0.048 for the comparison ratio (p0.05), there is a statistically significant difference between the CNN and the TIM transformation. According to the findings, the suggested CNN model is 93% accurate on the dataset, with no rejected samples. 2023 IEEE.
- Source
- 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023, pp. 1490-1494.
- Date
- 2023-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Biometric Security; Cloud.; CNN; Human Fingerprint recognition; Novel Detection; TIM
- Coverage
- Minocha S., Galgotias University, Department of Cse, Uttar Pradesh, Greater Noida, India; Krishnachalitha K.C., Christ (Deemed to Be University), School of Business and Management, Bangalore, India; Gupta Chancellor S., Sanskriti University, Department of Management, Uttar Pradesh, Mathura, India; Alatba S.R., Al-Turath Universiy College, Computer Science Department, Baghdad, Iraq; Pund S.S., Shri Ramdeobaba College of Engineering and Management, Department of Industrial Engineering, Maharashtra, Nagpur, India; Alfurhood B.S., Princess Nourah Bint Abdulrahman University, College of Computer and Information Sciences, Department of Computer Sciences, Saudi Arabia
- Rights
- Restricted Access
- Relation
- ISBN: 979-835039926-4
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Minocha S.; Krishnachalitha K.C.; Gupta Chancellor S.; Alatba S.R.; Pund S.S.; Alfurhood B.S., “A finger print recognition using CNN Model,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19805.