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Deep Learning Ensemble Approach to Age Group Classification Based On Fingerprint Pattern

Yıl 2023, , 76 - 84, 29.10.2023
https://doi.org/10.54569/aair.1303116

Öz

The age distribution of a population is extremely valuable to any business or country. In order to make decisions with regard to facility allocations and other social economic developmental issues, determination of age group distribution information is essential. The attempt to deceive others about one's age is a significant problem in the sporting world, as well as in other organizations and electoral processes. Therefore, there is a requirement for an age detection system, which is required to authenticate individual claims. Fingerprint-based age estimate research is scarce due to paucity of dataset. However, there are indications that fingerprints can reveal age demographic. This study's objective is to live-scan fingerprint images in order to identify age groups. This study proposed novel Dynamic Horizontal Voting Ensemble (DHVE) with Hybrid of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) as the base learner. The method constructs a horizontal voting ensemble for prediction by dynamically determining proficient models based on the validation accuracy metric during base learner training on the training set. Accuracy, recall, precision, and the F1 score were employed as standard performance metrics to measures the model's performance analysis. According to this study, predicting individual age group was accurate to a degree of above 91%. The DHVE network performed well due to the design of the layers. Integration of dynamic selection approach to horizontal voting ensemble improved the average performance of the model output.

Destekleyen Kurum

N/A

Proje Numarası

N/A

Kaynakça

  • Galbally J, Haraksim R, Beslay L. “A Study of Age and Ageing in fingerprint Biometrics”, IEEE Transactions on Information Forensics and Security, 14(5), 1351–1365, 2019.
  • Kumar S, Rani S, Jain A, Verma C, Raboaca MS, Illés Z, Neagu BC. “Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System”, Sensors, 22(14), 51-60, 2022.
  • Medina-Sotomayor P, Pascual MA, Camps AI. “Accuracy of four digital scanners according to scanning strategy in complete-arch impressions”, PLOS ONE, 13(9), 2018.
  • Al-Refoa A, Alshraideh M, Sharieh A. “A New Algorithm for Locating and Extracting Minutiae from Fingerprint Images”, Pattern Recognition and Image Analysis, 29(2), 268–279, 2019.
  • Yang W, Wang S, Hu J, Zheng G, Valli C. “Security and Accuracy of Fingerprint-Based Biometrics: A Review”, Symmetry, 11(2), 141, 2019.
  • Bahmani K, Plesh R, Johnson P, Schuckers S, Swyka T. “High Fidelity Fingerprint Generation: Quality, Uniqueness, And Privacy”, IEEE International Conference on Image Processing (ICIP) 2021.
  • Faridah Y, Nasir H, Kushsairy AK, Safie SI, Khan S, Gunawan TS. “Fingerprint Biometric Systems”, Trends in Bioinformatics, 9(2), 52–58, 2016.
  • Abdelwhab A, Viriri S. “A Survey on Soft Biometrics for Human Identification”, Machine Learning and Biometrics. 2(3), 2018.
  • Das AK, Antitza D, Francois B. “Mitigating Bias in Gender, Age and Ethnicity Classification: A Multi-task Convolution Neural Network Approach”, Lecture Notes in Computer Science, 573–585, 2018.
  • Lee H, Hwang JY, Kim DI, Lee S, Lee SH, Shin JS. “Understanding Keystroke Dynamics for Smartphone Users Authentication and Keystroke Dynamics on Smartphones Built-In Motion Sensors”, Security and Communication Networks, 2018.
  • Kloppenburg, S., Van der Ploeg, I. “Securing Identities: Biometric Technologies and the Enactment of Human Bodily Differences. Science as Culture, 1–20, 2018.
  • Gnanasivam P, Muttan S. “Fingerprint Gender Classification using Wavelet Transform and Singular Value Decomposition”, ArXiv (Cornell University) 2012.
  • Eyüp BC, Seref S, Ramazan C, Oner K. “Age Estimation from Fingerprints: Examination of the Population in Turkey”, International Conference on Machine Learning and Applications”, 4(1), 2014.
  • Marasco E, Luca L, Bojan C. “Exploiting quality and texture features to estimate age and gender from fingerprints”, Proceedings of SPIE 2014.
  • Saxena A, Vijay KC. “Multi-resolution texture analysis for fingerprint based age-group estimation”, Multimedia Tools and Applications, 77(5), 2018.
  • Das S, De Ghosh I, Chattopadhyay A. “Deep Age Estimation Using Sclera Images in Multiple Environment”, Advances in Intelligent Systems and Computing, 93–102, 2021.
  • Iloanusi ON, Ejiogu UC. “Gender classification from fused multi-fingerprint types: A Global Perspective”, Information Security Journal, 1–11, 2020.
  • Ibrahim AM, Eesee AK, Al-Nima RRO. “Deep fingerprint classification network”, TELKOMNIKA (Telecommunication Computing Electronics and Control), 19(3) 893-897, 2021.
  • Deshmukh DK, Patil SS. “Fingerprint-Based Gender Classification by Using Neural Network Model”, Applied Computer Vision and Image Processing, 318–325, 2020.
  • Xuan Z, Liu H, Li C, Liu Y. “Wavelet Bilateral Filter Algorithm-Based High-Frequency Ultrasound Image Analysis on Effects of Skin Scar Repair”, Scientific Programming, 1–7, 2021.
  • Cruz, R. M. O., Sabourin, R., Cavalcanti, GDC. ‘Dynamic classifier selection: Recent advances and perspectives. Information Fusion, 41, 195–216, 2017.
  • Ko AHR, Sabourin R, Britto Jr, Alceu S. “From dynamic classifier selection to dynamic ensemble selection”, Pattern Recognition, 41(5), 1718–1731, 2008.
Yıl 2023, , 76 - 84, 29.10.2023
https://doi.org/10.54569/aair.1303116

Öz

Proje Numarası

N/A

Kaynakça

  • Galbally J, Haraksim R, Beslay L. “A Study of Age and Ageing in fingerprint Biometrics”, IEEE Transactions on Information Forensics and Security, 14(5), 1351–1365, 2019.
  • Kumar S, Rani S, Jain A, Verma C, Raboaca MS, Illés Z, Neagu BC. “Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System”, Sensors, 22(14), 51-60, 2022.
  • Medina-Sotomayor P, Pascual MA, Camps AI. “Accuracy of four digital scanners according to scanning strategy in complete-arch impressions”, PLOS ONE, 13(9), 2018.
  • Al-Refoa A, Alshraideh M, Sharieh A. “A New Algorithm for Locating and Extracting Minutiae from Fingerprint Images”, Pattern Recognition and Image Analysis, 29(2), 268–279, 2019.
  • Yang W, Wang S, Hu J, Zheng G, Valli C. “Security and Accuracy of Fingerprint-Based Biometrics: A Review”, Symmetry, 11(2), 141, 2019.
  • Bahmani K, Plesh R, Johnson P, Schuckers S, Swyka T. “High Fidelity Fingerprint Generation: Quality, Uniqueness, And Privacy”, IEEE International Conference on Image Processing (ICIP) 2021.
  • Faridah Y, Nasir H, Kushsairy AK, Safie SI, Khan S, Gunawan TS. “Fingerprint Biometric Systems”, Trends in Bioinformatics, 9(2), 52–58, 2016.
  • Abdelwhab A, Viriri S. “A Survey on Soft Biometrics for Human Identification”, Machine Learning and Biometrics. 2(3), 2018.
  • Das AK, Antitza D, Francois B. “Mitigating Bias in Gender, Age and Ethnicity Classification: A Multi-task Convolution Neural Network Approach”, Lecture Notes in Computer Science, 573–585, 2018.
  • Lee H, Hwang JY, Kim DI, Lee S, Lee SH, Shin JS. “Understanding Keystroke Dynamics for Smartphone Users Authentication and Keystroke Dynamics on Smartphones Built-In Motion Sensors”, Security and Communication Networks, 2018.
  • Kloppenburg, S., Van der Ploeg, I. “Securing Identities: Biometric Technologies and the Enactment of Human Bodily Differences. Science as Culture, 1–20, 2018.
  • Gnanasivam P, Muttan S. “Fingerprint Gender Classification using Wavelet Transform and Singular Value Decomposition”, ArXiv (Cornell University) 2012.
  • Eyüp BC, Seref S, Ramazan C, Oner K. “Age Estimation from Fingerprints: Examination of the Population in Turkey”, International Conference on Machine Learning and Applications”, 4(1), 2014.
  • Marasco E, Luca L, Bojan C. “Exploiting quality and texture features to estimate age and gender from fingerprints”, Proceedings of SPIE 2014.
  • Saxena A, Vijay KC. “Multi-resolution texture analysis for fingerprint based age-group estimation”, Multimedia Tools and Applications, 77(5), 2018.
  • Das S, De Ghosh I, Chattopadhyay A. “Deep Age Estimation Using Sclera Images in Multiple Environment”, Advances in Intelligent Systems and Computing, 93–102, 2021.
  • Iloanusi ON, Ejiogu UC. “Gender classification from fused multi-fingerprint types: A Global Perspective”, Information Security Journal, 1–11, 2020.
  • Ibrahim AM, Eesee AK, Al-Nima RRO. “Deep fingerprint classification network”, TELKOMNIKA (Telecommunication Computing Electronics and Control), 19(3) 893-897, 2021.
  • Deshmukh DK, Patil SS. “Fingerprint-Based Gender Classification by Using Neural Network Model”, Applied Computer Vision and Image Processing, 318–325, 2020.
  • Xuan Z, Liu H, Li C, Liu Y. “Wavelet Bilateral Filter Algorithm-Based High-Frequency Ultrasound Image Analysis on Effects of Skin Scar Repair”, Scientific Programming, 1–7, 2021.
  • Cruz, R. M. O., Sabourin, R., Cavalcanti, GDC. ‘Dynamic classifier selection: Recent advances and perspectives. Information Fusion, 41, 195–216, 2017.
  • Ko AHR, Sabourin R, Britto Jr, Alceu S. “From dynamic classifier selection to dynamic ensemble selection”, Pattern Recognition, 41(5), 1718–1731, 2008.
Toplam 22 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Zeka
Bölüm Araştırma Makalesi
Yazarlar

Olufunso Olorunsola 0000-0003-1686-6055

Oluwaseyi Olorunshola 0000-0001-6855-9116

Proje Numarası N/A
Erken Görünüm Tarihi 23 Ekim 2023
Yayımlanma Tarihi 29 Ekim 2023
Kabul Tarihi 28 Eylül 2023
Yayımlandığı Sayı Yıl 2023

Kaynak Göster

IEEE O. Olorunsola ve O. Olorunshola, “Deep Learning Ensemble Approach to Age Group Classification Based On Fingerprint Pattern”, Adv. Artif. Intell. Res., c. 3, sy. 2, ss. 76–84, 2023, doi: 10.54569/aair.1303116.

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