An Optimize Multimodal Biometric Authentication System For Low Classification Error Rates Using Face and Fingerprint
| Autor: | Manju Dhanraj Pawar, R. D. Kokate, V.R. Gosavi |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Minutiae
Biometrics Computer science business.industry Fingerprint (computing) Feature extraction Scale-invariant feature transform Pattern recognition Facial recognition system ComputingMethodologies_PATTERNRECOGNITION Face (geometry) Pattern recognition (psychology) Artificial intelligence business |
| Zdroj: | SSRN Electronic Journal. |
| ISSN: | 1556-5068 |
| DOI: | 10.2139/ssrn.3883852 |
| Popis: | Now a days, the biometric system is playing a significant role, especially in the banking sector, law enforcement region. In previous times the unimodal system was used for biometrics but now the trend is for multimodal biometrics which is an important part of pattern recognition and grabs a lot of attention of the people in the research area. A lot of work is done on unimodal systems but very less work is done on multimodal characteristics. This paper deals with the secure authentication process using fingerprint and face recognition multimodal systems for high classification accuracy and low error rates. The feature extraction and optimization are done using SIFT & PSO for the facial category and the ridges and minutiae extractions are done for the fingerprint. The proposed solution is evaluated on the Ollivati dataset for faces and the FVC200 dataset for fingerprints. From the result and discussions, it can be seen that the proposed approach can attain an accuracy of approximately 99.2% which is our desired objective, and low false acceptance and rejection rates which increases the authenticity of our multimodal system. |
| Databáze: | OpenAIRE |
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