International Journal of Data Science and Analysis
Volume 6, Issue 2, April 2020, Pages: 64-68
Received: Dec. 9, 2019;
Accepted: Dec. 16, 2019;
Published: May 29, 2020
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Shivanand Sharanappa Gornale, Department of Computer Science, Rani Channamma University, Belagavi, Karnataka, India
Abhijit Patil, Department of Computer Science, Rani Channamma University, Belagavi, Karnataka, India
Kruti Ramchandra, Department of Computer Science, Jain University, Bangalore, India
In the recent past with the rapid growing technology security problem is ubiquitous to our daily life pertinent to it, now a day the usage of biometrics is becoming inevitable. Correspondingly, the field of biometrics has gained tremendous acceptance because of its individualistic and authentication capabilities. In many practical scenario the multimodal-based gender estimation will helps to increase the security and efficiency of other biometrics system. Likewise, in contrast to it uni-modal biometric, the multimodal biometrics system would be very difficult to spoof because of its multiple distinct biometrics features. Gender identification using biometrics traits are mainly used for reducing the search space list, indexing and generating statistical reports etc In this paper, a robust multimodal gender identification method based on the deep features are computed using the off-the-shelf pre-trained deep convolution neural network architecture based on AlexNet. The proposed model consists of 20 subsequent layers which contain different window size of convolutional layers following with fully connected layers for feature extraction and classification. Extensive experiments have been conducted on a homologous SDUMLA-HMT (Shandong University Group of Machine Learning and Applications) multimodal database with 15052 images. The proposed method achieved the accuracy of 99.9% which outperforms the results noticed in the literature.
Shivanand Sharanappa Gornale,
Multimodal Biometrics Data Analysis for Gender Estimation Using Deep Learning, International Journal of Data Science and Analysis. Special Issue: Multimodal Biometric Data Analysis.
Vol. 6, No. 2,
2020, pp. 64-68.
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