Multimodal Biometrics Data Analysis for Gender Estimation Using Deep Learning
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
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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.
AlexNet, Biometrics, Convolutional Neural Network, Deep Neural Network, Gender Estimation, Multimodal, SDUMLA-HMT
To cite this article
Shivanand Sharanappa Gornale, Abhijit Patil, Kruti Ramchandra, 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. doi: 10.11648/j.ijdsa.20200602.11
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S. S. Gornale, Abhijit Patil and Veersheety C. “Fingerprint Based Gender Identification Using DWT and Gabor Filters”, International Journal of Computer Applications Vol. 152 Issue 4, page. 34-37 Oct-2016.
Kruti R, Abhijit Patil and S. S. Gornale, “Fusion of Features and Synthesis Classifiers for Gender Classification using Fingerprints”, International Journal of Computer Sciences and Engineering (IJCSE) ISSN: 2347-2693, Vol-7 Issue-5, May 2019.
Kruti R, Abhijit Patil and S. S. Gornale,”Fusion of Local Binary Pattern and Local Phase Quantization features set for Gender Classification using Fingerprints”, International Journal of Computer Sciences and Engineering, vol. 7, issue 1, pp. 22-29 Feb -2019.
S. S Gornale, Malikarjun Hangarge, Rajmohan P, Kruthi R, “Haralick Feature Descriptors for Gender Classification Using Fingerprints: A Machine Learning Approach”, International Journal of Advanced Research in Computer Science and Software Engineering”, Volume 5, Issue 9, ISSN: 2277 128X, PP-72-78 September 2015.
K. S. Arun and K. S. Sarath,” A machine learning approach for fingerprint based gender identification, 2011 IEEE Recent Advances in Intelligent Computational Systems, ISBN: 978-1-4244-9478-1, pp: 22-24 Sept. 2011.
Zhang H., Sun Z., Tan T., Wang J. (2011) “Ethnic Classification Based on Iris Images”, In: Sun Z., Lai J., Chen X., Tan T. (eds) Biometric Recognition. CCBR 2011. Lecture Notes in Computer Science, Vol 7098. Springer, Berlin, Heidelberg 2011.
S. Aryanmehr, Fanny Dufoss, Farsad Zamani Boroujeni. “CVBL IRIS Gender Classification Database Image Processing and Biometric Research, Computer Vision and Biometric Laboratory (CVBL)”, 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC). 2018.
Shruti Nagpal, Mayank Vatsa; Richa Singh; Afzel Noore; Angshul Majumdar Maneet Singh, “Gender and ethnicity classification of Iris images using deep class-encoder”, 2017 IEEE International Joint Conference on Biometrics (IJCB), DOI: 10.1109/BTAS.2017.8272755, ISSN: 2474-9699, Denver, CO, USA. 2017.
Tapia J. E., Perez C. A., Bowyer K. W. (2015) “Gender Classification from Iris Images Using Fusion of Uniform Local Binary Patterns. In: Agapito L., Bronstein M., Rother C. (eds) Computer Vision - ECCV 2014 Workshops. ECCV 2014. Lecture Notes in Computer Science, vol. 8926. Springer, Cham.
W Ming and Y Yuan, “Gender Classification Based on Geometrical Features of Palmprint Images”, The Scientific World Journal vol. 2014. Article Id: 734564, pp.: 7 2014.
S. S Gornale, Abhijit Patil and Kruti R, “Fusion Of Gabor Wavelet And Local Binary Patterns (LBP) Features Sets For Gender Identification Using Palmprints”, International journal of Imaging Science and Engineering, Vol. 10, Issue No. 2, April 2018.
Zhihuai Xie, Zhenhua G and Chengshan Q, “Palmprint gender classification by Convolution neural network”, IET Computer Vision, Vol 12, Issue 4 pp: 476-483, 2018.
Shivanand S Gornale, Abhijit Patil and Mallikarjun Hangarge, “Binarized Statistical Image Feature Set for Palmprint based Gender Identification”, Book of Abstract of First International Conference on Machine Learning, Image Processing, Network Security and Data Sciences (MIND-2019) pp.: 61, held on 3rd- 4th march 2019 at National Institute of Technology, Kurukshetra, India.
Shivanand S Gornale, Abhijit Patil, Mallikarjun Hangarge and Rajmohan P, “Automatic Human Gender Identification using Palmprint”. Smart Computational Strategies: Theoretical and Practical Aspects. Springer, Singapore, Online ISBN 978-981-13-6295-8, Print ISBN 978-981-13-6294-1, 22-march-2019.
Paul Viola and MichaelJ. Jones. 2004. Robust Real-Time Face Detection. International Journal of Computer Vision Vol. 57, Issue 2 pp. 137–154 (2004).
Len Bui; Dat Tran; Xu Huang; Girija Chetty, “Classification of gender and face based on gradient faces”, 3rd European Workshop on Visual Information Processing, 10.1109/EuVIP.2011.6045544 Paris, France -July 2011.
Arnulf B. A. Graf and Felix A. Wichmann, “Gender Classification of Human Faces”, Biologically Motivated Computer Vision, eds. H. H. B¨ulthoff, S.-W. Lee, T. A. Poggio and C. Wallraven, LNCS 2525, pp.: 491-501, 2002, Springer Verlag, Heidelberg.
S. Ravi, S. Wilson, “Face Detection with Facial Features and Gender Classification Based On Support Vector Machine”, 2010 Special Issue - International Journal of Imaging Science and Engineering.
Abul Hasnat; Santanu Haider; Debotosh Bhattacharjee; Mita Nasipuri, “A proposed system for gender classification using lower part of face image:, 2015 International Conference on Information Processing (ICIP), DOI: 10.1109/INFOP.2015.7489451, ISBN: 978-1-4673-7758-4, Pune, India.
Hadi Harb, Liming Chen, “Voice-Based Gender Identification in Multimedia Applications”, Journal of Intelligent Information Systems, vol. 24: pp.: 179, 2005.
Rafik Djemili; Hocine Bourouba; Mohamed Cherif Amara Korba, “A speech signal based gender identification system using four classifiers”, 2012 International Conference on Multimedia Computing and Systems, ISBN no. 978-1-4673-1520-3, 2012.
Daniel Reid, Sina Samangooei, Cunjian Chen, Mark Nixon, and Arun Ross. 2013. So. biometrics for surveillance: an overview. Machine learning: theory and applications. Elsevier pp: 327–352, 2013.
L Walawalkar, M. Yeasin, A. Narasimhamurthy, and R. Sharma. Support vector learning for gender classification using audio and visual cues. International Journal of Pattern Recognition and Artificial Intelligence, 17 (3): 417–439, 2003.
X. Li, X. Zhao, H. Liu, Y. Fu and Y. Liu, "Multimodality gender estimation using Bayesian hierarchical model," 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, Dallas, TX, 2010, pp.: 5590-5593. doi: 10.1109/ICASSP.2010.5495242.2010.
Mohamed Abouelenien, Veronica Perez-Rosas, Rada Mihalcea, and Mihai Burzo, “Multimodal Gender Detection”, in Proceedings of ICMI ’17, Glasgow, UK, November, pp.: 302-311. ISBN no. 978-1-4503-5543-8.2017. 2017.
A. Hadid and M. Pietikainen, "Combining motion and appearance for gender classification from video sequences", 2008 19th International Conference on Pattern Recognition, Tampa, Florida, 2008, pp: 1-4. doi: 10.1109/ICPR.2008.4760995.
Caifeng Shan and Shaogang Gong and McOwan, Peter W, “Learning Gender from Human Gaits and Faces”, 2007 IEEE Conference on Advanced Video and Signal Based Surveillance Washington, DC, USA. pp: 505-510, isbn 978-1-4244-1695-0.
Abhijit Patil, Kruthi R and Shivanand Gornale, " Analysis of Multi-modal Biometrics System for Gender Classification Using Face, Iris and Fingerprint Images", International Journal of Image, Graphics and Signal Processing (IJIGSP), Vol. 11, No. 5, pp. 34-43, 2019. DOI: 10.5815/ijigsp.2019.
Shivanand S Gornale, Abhijit Patil and Kruthi R, “Multimodal Biometrics Data Based Gender Classification using Machine Vision”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Vol-8 Issue-11, September 2019.
Maneet Singh, Richa Singh, Arun Ross, A comprehensive overview of biometric fusion, Information Fusion, Volume 52, 2019, pp. 187-205, ISSN 1566-2535,
Nour Eldeen M. Khalifa, Mohamed Hamed N. Taha and Hamed Nasr Eldin T. Mohamed, “Deep Iris: Deep Learning for Gender Classification Through Iris Patterns”, Acta Informatica Medica, Vol. 22 Issue 2, pp-96-102, June 2019, doi: 10.5455/aim.2019.27.96-102.
Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems. Curran Associates Inc.; 2012: 1097-1105.
Yilong Yin, Lili Liu, and Xiwei Sun,” SDUMLA-HMT: A Multimodal Biometric Database”, The 6th Chinese Conference on Biometric Recognition (CCBR 2011), LNCS 7098, pp.: 260-268, Beijing, China, 2011.
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