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Smart city article details
| Title | Face And Iris-Based Secured Authorization Model Using Cnn |
|---|---|
| ID_Doc | 25975 |
| Authors | Kumar M.; Bhola A.; Tiwari A.; Gulhane M. |
| Year | 2023 |
| Published | Multimodal Biometric and Machine Learning Technologies: Applications for Computer Vision |
| DOI | http://dx.doi.org/10.1002/9781119785491.ch14 |
| Abstract | Biometric security is emerging as a prominent issue worldwide in data security. In the cyber world, multimodal biometric solutions for human identity detection in uncontrolled environments are gaining significant attention. This work's primary purpose is to provide multimodal biometric authentication. When compared to unimodal biometrics, which employs a single biometric indicator such as a fingerprint, face, palm print, or iris, multimodal authentication provides more effective authentication. The multimodal biometric approach with improved identification rate for smart cities remains a complex problem. This chapter offers improved multimodal biometric recognition for smart cities by combining iris and facial biometrics. In this suggested work, the face and iris of an individual are connected at the matching level, and the score level for the automatic identification of a person will calculate. By utilizing neural network-based architecture, this is accomplished. With the suggested method, accuracy rates are higher at 99.03%, and equal-mistake rates are lower at 0.15%. Multimodal biometrics is the best answer for all industries that require more precision and security. © 2023 Scrivener Publishing LLC. All rights reserved. |
| Author Keywords | Authentication; Databases; Face recognition; Image edge detection; Iris recognition |
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