A Comparison between Backpropagation Neural Network and Seven Moments for More Accurate Fingerprint Video Frames Recognition
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Abstract
In this modern age of electronic interactions, more secure methods are required to protect vital information. Passwords are indeed a prominent and secure method, but they are subject to being forgotten, especially if they are long and complex. A more efficient way is the use of human fingerprints, which are unique to each person. No two people would have the same fingerprint even if they were a twin, which makes it a very secure method that cannot be duplicated or forgotten. This research aims to compare seven moments and backpropagation for more accurate fingerprint recognition within video frames. The first method is the "seven moments," and the second method is the Backpropagation Neural Network (BPNN), both applied to the interest points that are extracted from each frame. For extracting the interest points from each one of the frames, Smallest Univalue Segment Assimilating Nucleus (SUSAN), a corner detector, was employed. Multiple examples of video frames were used in comparison, and the findings demonstrated that the BPNN approach was more accurate even when the fingerprint had a significant amount of corrupted data or unclear image pixels.
Received 20/03/2023
Revised 16/09/2023
Accepted 18/09/2023
Published Online First 20/04/2024
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This work is licensed under a Creative Commons Attribution 4.0 International License.
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Alaa N, Ekhlas F. Hiding the Type of Skin Texture in Mice based on Fuzzy Clustering. Baghdad. Sci J. 2020; 17(3): 967-972. http://dx.doi.org/10.21123 /bsj.2020.17.3(Suppl.).0967.
Saad O, Kadhim H. Fingerprint Recognition Based on Gabor Features Extraction. J Edu. Sci Thiq Univ. 2020; 10(2): 96-102.
Uttam U, Malemath V. A Study on Automatic Latent Fingerprint Identification System. J. Comp. Sci. Res. 2022; 4(1): 38-50. https://doi.org/10.30564/ jcsr.v4i1.4388.
Jannis P, Christian R, Nicolas B, Christoph B, Marian M. An overview of touchless 2D fingerprints recognition. J. Img. Vid. Proc. 2021; 8:1-28. https://doi.org/10.1186/s13640-021-00548-4.
Sulavko A. Bayes-Minkowski measure and building on its basis immune machine learning algorithms for biometric facial identification. J Phys Conf Ser. 2020; 1546(1): 1-7. https://doi.org/10.1088/1742-6596/1546/1/012103.
Haroon S, Tariqullah J, Amjad A, Naveed A, Abid M, Ruhul A. Fingerprint image enhancement using multiple filters. Peer J Comp Sci. 2023; 9: 1-19.http://doi.org/10.7717/peerj-cs.1183.
Zihao L, Yizhi W, Zhong Y, Xiaomin T, Lixin Z, Xiao W, Jianpeng Y, Shanshan G, Lingyi H, and Yang Z. A novel fingerprint recognition method based on a Siamese neural network. J Intel Sys. 2022; 31: 690–705. https://doi.org/10.1515/jisys-2022-0055.
Jannis P, Rolf H, Christian R, Nicolas B, Christoph B. Mobile Contactless Fingerprint Recognition: Implementation, Performance and Usability Aspects. Int. J Sens. 2022; 22(3): 1-21. https://doi.org /10. 3390/s22030792.
Ashraf T., Hasanen S., Mohammad N. Impostor Detection Based Finger Veins Applying. Iraq. J. Comp. Comm. Cont. Syst. Eng. (IJCCCE), 2021; 21(3):98-111.https://doi.org/https://doi.org/10.33103 / uot.ijccce.21.3.9.
Ekhlas F. Compare Between Histogram Similarity and Histogram Differencing For More Brief Key Frames Extraction from Video Stream. J Phys.: Conf Ser. 2021; 1897: 1-10. https://doi.org/10.1088/1742-6596/1897/1/012022.
Xiaohua X, Haoming X, Yusong C, Zhaokai G, Zainan J. Feature Extraction and Matching of Humanoid-Eye Binocular Images Based on SUSAN-SIFT Algorithm. Biomimetics. 2023; 8(2): 1-16. https://doi.org/10.3390/biomimetics8020139.
Muna G., Matheel E. Multimodal video abstraction into a static document using deep learning. Int. J. Elec. Comp. Eng. (IJECE) 2023; 13(3): 2752-2760. https://doi.org/10.11591/ijece.v13i3 .
Zan T, Liu Z, Wang H, et al. Control chart pattern recognition using the convolutional neural network. J Intell Manuf. 2020; 31(3): 703-716. https://doi.org/10.1007/s10845-019-01473-0.
Anuradha J. Big data based stock trend prediction using deep CNN with reinforcement-lstm model. Int. J. Syst. Assur. Eng. Manag. 2021; 5(1):1–11. http://dx.doi.org/10.1007/s13198-021-01074-2.
Mohd W, Shahrul N, Amir N, Mohd Z, Nuraminah R, Mohd S. Moment Invariants Technique for Image Analysis and Its Applications: A Review. Int. Conf. Eng. Tech. (ICoEngTech), J Phy. 2021; 1962 https://doi.org/10.1088/1742-6596/1962/1/012028.
Iulia N, Simona M, Luminita M. Image Moment-Based Features for Mass Detection in Breast US Images via Machine Learning and Neural Network Classification Models. Inventions. 2022; 7(2): 1-11. https://doi.org/10.3390/inventions7020042.
Ekhlas F, Suhiar M. Using Fuzzy Clustering to Detect the Tumor Area in Stomach Medical Images. Baghdad Sci J. 2021; 18(4): 1294-1302. http://dx.doi.org/10.21123/bsj.2021.18.4.1294.