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- W2783409026 abstract "Biometric is an automated process of identifying or verifying an individual based upon his or her behavioral or physical characteristics. Biometrics speaks loud in the Area of Security, Banking and Forensic department. Single modality based recognition verification is not very robust while combining information from various biometric modalities provides better performance. In our Proposed work Modalities such as Finger print, Palm print and Finger Knuckle prints are used for authenticate a personal. Grey Level Co Occurrence Matrix (GLCM) feature extraction Technique is used to extract the unique characteristics of these Modalities. Extracted Features are then fused in Feature level. During Classification, use of optimized Artificial Neural Network (ANN) with particle swarm optimization (PSO) algorithm recognizes a person with high level security, specificity and sensitivity." @default.
- W2783409026 created "2018-01-26" @default.
- W2783409026 creator A5023765567 @default.
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- W2783409026 date "2017-03-01" @default.
- W2783409026 modified "2023-09-27" @default.
- W2783409026 title "Feature level fusion approach for personal authentication in multimodal biometrics" @default.
- W2783409026 cites W2019667142 @default.
- W2783409026 cites W2033150480 @default.
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- W2783409026 doi "https://doi.org/10.1109/iconstem.2017.8261272" @default.
- W2783409026 hasPublicationYear "2017" @default.
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