Hybridization of Feature Level Fusion with Ant Colony Optimization in Multimodal Biometrics
Sakuntla Meena1, Amit Doegar2
1Sakuntla Meena, Computer Science and Engineering, National Institute of Technical Teacher Training and Research, Chandigarh, India.
2Amit Doegar, Computer Science and Engineering, National Institute of Technical Teacher Training and Research, Chandigarh, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 2846-2851 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8781088619/2019©BEIESP | DOI: 10.35940/ijeat.F8781.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In biometric system, multimodal biometrics provides stronger security as compared to unimodal biometrics. Even though multimodal biometric improves the accuracy and reliability of the system, but requires large memory storage and consumes numerous execution time due to use of high dimensionality datasets. Search is being an NP-hard problem in biometrics, which garnish an attention for research in biometric system. Due to NP-hard nature of searching in biometric, accurate solutions could not be discovered in limited time. Therefore, researchers use heuristic or random search methods such as PSO, GA, ACO and Cuckoo search etc. to obtain optimal or approximate optimal solutions for such problems. This paper proposes a hybrid approach of feature level fusion in biometric system with Ant Colony Optimization based feature sub selection method to aiming to improve performance. The median filter and morphological operations are used for pre-processing of finger vein and fingerprint images respectively. Confusion matrix plot with equal error rate and accuracy are the evaluation parameters.
Keywords: ACO, KNN, Median Filter, Morphological Operations, Random Forest Classifier.