Convolution Neural Network Based Deep Feature Fusion for Palmprint and Handvein
Shreyas Rangappa1, Naveena C2, H K Chethan3, K S Ragunnandan4, Sunil C5
1Shreyas Rangappa, Dept. of Computer Science, SJBIT, Bangalore, India. Naveen C, Dept. of Computer Science, SJBIT, Bangalore, India.
2Chethan H K, Dept. of Computer Science, University of Mysore, Mysore,
3Raghunandan K S, Dept. of Computer Science, University of Mysore, Mysore, India.
4Sunil C, Dept. of Computer Science, University of Mysore, Mysore, India.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 2383-2386 | Volume-9 Issue-1, October 2019 | Retrieval Number: F92171088619/2019©BEIESP | DOI: 10.35940/ijeat.F9217.109119
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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: A distinctive Deep Convolution Neural Network (CNN) outline architecture model is presented in this work which proficiently signify complex image features. With two convolution layers implementation of feature extraction is carried out successfully and provides a well-built biometric verification system by using physiological traits Palm print and Hand vein for development of our system. The formation of CNN hyper parameters carried out in numerous experiments such as filters and its size in each layer, number of convolution layers needed, batch size, iterations, learning rate is a paramount and epochs defining these factors are truly dependent on the nature of data and its size. The new method presented in our model has been part with and it gives 99% of GAR in biometric verification of unimodal system and extremely the experimentation has condensed results when combined with deep feature extraction and classification outcomes.
Keywords: Handvein, Palmprint, Convolution Neural Network.