Research Article
BibTex RIS Cite
Year 2023, , 78 - 87, 30.01.2023
https://doi.org/10.17694/bajece.1171905

Abstract

References

  • [1] M. M. J. A. S. E. J. Fahmy, "Palmprint recognition based on Mel frequency Cepstral coefficients feature extraction," vol. 1, no. 1, pp. 39-47, 2010.
  • [2] M. A. Alsmirat, F. Al-Alem, M. Al-Ayyoub, Y. Jararweh, and B. Gupta, "Impact of digital fingerprint image quality on the fingerprint recognition accuracy," Multimedia Tools and Applications, vol. 78, no. 3, pp. 3649-3688, 2019.
  • [3] H. Chen and B. Bhanu, "Contour matching for 3D ear recognition," in 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05)-Volume 1, 2005, vol. 1: IEEE, pp. 123-128.
  • [4] Ž. Emeršič, V. Štruc, and P. Peer, "Ear recognition: More than a survey," Neurocomputing, vol. 255, pp. 26-39, 2017.
  • [5] M. Fischer, M. Rybnicek, and S. Tjoa, "A novel palm vein recognition approach based on enhanced local Gabor binary patterns histogram sequence," in 2012 19th International Conference on Systems, Signals and Image Processing (IWSSIP), 2012: IEEE, pp. 429-432.
  • [6] C. Wilson, Vein pattern recognition: a privacy-enhancing biometric. CRC press, 2010.
  • [7] W. Shu and D. Zhang, "Automated personal identification by palmprint," Optical Engineering, vol. 37, pp. 2359-2362, 1998.
  • [8] A. Michele, V. Colin, and D. D. J. P. C. S. Santika, "Mobilenet convolutional neural networks and support vector machines for palmprint recognition," vol. 157, pp. 110-117, 2019.
  • [9] X.-Q. Wu, K.-Q. Wang, and D. Zhang, "Wavelet based palm print recognition," in Proceedings. International Conference on Machine Learning and Cybernetics, 2002, vol. 3: IEEE, pp. 1253-1257.
  • [10] S. F. ABD RAZAK, "IMAGE ANALYSIS OF PALM (PALMISTRY)(HEALTH AND CHARACTERISTICS)," BACHELOR OF ENGINEERING, ELECTRICAL & ELECTRONICS ENGINEERING, Universiti Teknologi PETRONAS, TRONOH, PERAK, 2006.
  • [11] J. Saint-Germain, Lovers Guide to Palmistry: Finding Love in the Palm of Your Hand. Llewellyn Worldwide, 2008.
  • [12] M. M. Houck and J. A. Siegel, Fundamentals of forensic science. Academic Press, 2009.
  • [13] A. R. Jackson and J. M. Jackson, Forensic science. Pearson Education, 2008.
  • [14] A. Younesi and M. C. J. P. C. S. Amirani, "Gabor filter and texture based features for palmprint recognition," vol. 108, pp. 2488-2495, 2017.
  • [15] A. K. Jain, A. Ross, and S. Prabhakar, "An introduction to biometric recognition," IEEE Transactions on circuits and systems for video technology, vol. 14, no. 1, pp. 4-20, 2004.
  • [16] Y. Bulatov, S. Jambawalikar, P. Kumar, and S. Sethia, "Hand recognition using geometric classifiers," in International Conference on Biometric Authentication, 2004: Springer, pp. 753-759.
  • [17] L. Fei, B. Zhang, W. Zhang, and S. J. I. S. Teng, "Local apparent and latent direction extraction for palmprint recognition," vol. 473, pp. 59-72, 2019.
  • [18] J. Cui, "Multispectral fusion for palmprint recognition," Optik-International Journal for Light, vol. 124, no. 17, pp. 3067-3071, 2013.
  • [19] S. Zhang and X. Gu, "Palmprint recognition method based on score level fusion," Optik-International Journal for Light and Electron Optics, vol. 124, no. 18, pp. 3340-3344, 2013.
  • [20] S. Zhang and X. Gu, "Palmprint recognition based on the representation in the feature space," vol. 124, no. 22, pp. 5434-5439, 2013.
  • [21] J. Li, J. Cao, and K. Lu, "Improve the two-phase test samples representation method for palmprint recognition," Optik, vol. 124, no. 24, pp. 6651-6656, 2013.
  • [22] S. Zhao and B. Zhang, "Deep discriminative representation for generic palmprint recognition," Pattern Recognition, vol. 98, p. 107071, 2020.
  • [23] W. M. Matkowski, T. Chai, and A. W. K. Kong, "Palmprint recognition in uncontrolled and uncooperative environment," IEEE Transactions on Information Forensics Security, vol. 15, pp. 1601-1615, 2019.
  • [24] Z. Liu, Y. Yin, H. Wang, S. Song, and Q. Li, "Finger vein recognition with manifold learning," Journal of Network Computer Applications, vol. 33, no. 3, pp. 275-282, 2010.
  • [25] D. Hong, W. Liu, J. Su, Z. Pan, and G. Wang, "A novel hierarchical approach for multispectral palmprint recognition," Neurocomputing, vol. 151, pp. 511-521, 2015.
  • [26] M. D. Bounneche, L. Boubchir, A. Bouridane, B. Nekhoul, and A. Ali-Chérif, "Multi-spectral palmprint recognition based on oriented multiscale log-Gabor filters," Neurocomputing, vol. 205, pp. 274-286, 2016.
  • [27] F. Ma, X. Zhu, C. Wang, H. Liu, and X.-Y. Jing, "Multi-orientation and multi-scale features discriminant learning for palmprint recognition," Neurocomputing, vol. 348, pp. 169-178, 2019.
  • [28] D. Tamrakar, P. J. J. o. V. C. Khanna, and I. Representation, "Kernel discriminant analysis of Block-wise Gaussian Derivative Phase Pattern Histogram for palmprint recognition," vol. 40, pp. 432-448, 2016.
  • [29] D. Hong, W. Liu, X. Wu, Z. Pan, and J. Su, "Robust palmprint recognition based on the fast variation Vese–Osher model," Neurocomputing, vol. 174, pp. 999-1012, 2016.
  • [30] C. Turan and K.-M. Lam, "Histogram-based local descriptors for facial expression recognition (FER): A comprehensive study," Journal of visual communication and image representation, vol. 55, pp. 331-341, 2018.
  • [31] T. TUNCER and A. J. T. B. V. B. B. v. M. D. Engin, "Yerel İkili Örüntü Tabanli Veri Gizleme Algoritmasi: LBP-LSB," vol. 10, no. 1, pp. 48-53, 2017.
  • [32] L. Liu, S. Lao, P. W. Fieguth, Y. Guo, X. Wang, and M. J. I. T. o. I. P. Pietikäinen, "Median robust extended local binary pattern for texture classification," vol. 25, no. 3, pp. 1368-1381, 2016.
  • [33] H. Wan, L. Chen, H. Song, and J. Yang, "Dorsal hand vein recognition based on convolutional neural networks," in 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2017: IEEE, pp. 1215-1221.
  • [34] M. Afifi, "11K Hands: Gender recognition and biometric identification using a large dataset of hand images," Multimedia Tools and Applications, vol. 78, no. 15, pp. 20835-20854, 2019.
  • [35] N. A. Al-johania and L. A. Elrefaei, "Dorsal hand vein recognition by convolutional neural networks: Feature learning and transfer learning approaches," International Journal of Intelligent Engineering and Systems, vol. 12, no. 3, pp. 178-91, 2019.
  • [36] S.-J. Chuang, "Vein recognition based on minutiae features in the dorsal venous network of the hand," Signal, Image and Video Processing, vol. 12, no. 3, pp. 573-581, 2018.
  • [37] S. A. Radzi, M. K. Hani, and R. Bakhteri, "Finger-vein biometric identification using convolutional neural network," Turkish Journal of Electrical Engineering & Computer Sciences, vol. 24, no. 3, pp. 1863-1878, 2016.
  • [38] L. Wang, G. Leedham, and S.-Y. Cho, "Infrared imaging of hand vein patterns for biometric purposes," IET computer vision, vol. 1, no. 3-4, pp. 113-122, 2007.
  • [39] L. Wang, G. Leedham, and D. S.-Y. Cho, "Minutiae feature analysis for infrared hand vein pattern biometrics," Pattern recognition, vol. 41, no. 3, pp. 920-929, 2008.
  • [40] O. O. Iroanya, T. F. Egwuatu, O. T. Talabi, and İ. S. Ogunleye. “Sex Prediction Using Finger, Hand and Foot Measurements for Forensic Identification in a Nigerian Population”. Sakarya University Journal of Science, vol. 24 no. 3, pp. 432-445, 2020.

Classification Of Hand Images by Person, Age and Gender with The Median Robust Extended Local Binary Model

Year 2023, , 78 - 87, 30.01.2023
https://doi.org/10.17694/bajece.1171905

Abstract

Biometric technologies try to automatically recognize individuals by considering the physiological and behavioral characteristics of individuals. Although the methods used here are very diverse, the personal qualities used also vary. Facial features, finger and vein prints, iris, retina, ear, hand, and finger recognition are only some of the physiological features. It may be preferred to use one or more of these personal features to reduce the margin of error that may arise depending on the security level in the applications used. Biometric recognition systems have varying requirements in security systems applications. Fingerprint and iris recognition work well in applications that require high security levels, while applications that require low security levels are not suitable due to privacy concerns. On the other hand, identification from hand images is more accepted based on the idea that it does not have a very high distinctiveness. But it is sufficient for medium security applications. Apart from these, palm images have many advantages such as reliability, stability, user-friendliness, non-intrusiveness, and flexible use. In this study, it is aimed to identify people, determine their ages, and determine their gender by using both upper surface and inner surface images of right-left hand data of hand shape. For this purpose, images of both the inner surface of the hand (10) and the outer surface of the hand (10) of 100 different people were collected. This was done separately for the right and left hands, and a total of 3955 images were obtained. The features of these images were extracted using the Median Robust Extended Local Binary Model (MRELBP). Images are classified for person, age and gender. The results were 91.4%, 85.9% and 92.6%, respectively.

References

  • [1] M. M. J. A. S. E. J. Fahmy, "Palmprint recognition based on Mel frequency Cepstral coefficients feature extraction," vol. 1, no. 1, pp. 39-47, 2010.
  • [2] M. A. Alsmirat, F. Al-Alem, M. Al-Ayyoub, Y. Jararweh, and B. Gupta, "Impact of digital fingerprint image quality on the fingerprint recognition accuracy," Multimedia Tools and Applications, vol. 78, no. 3, pp. 3649-3688, 2019.
  • [3] H. Chen and B. Bhanu, "Contour matching for 3D ear recognition," in 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05)-Volume 1, 2005, vol. 1: IEEE, pp. 123-128.
  • [4] Ž. Emeršič, V. Štruc, and P. Peer, "Ear recognition: More than a survey," Neurocomputing, vol. 255, pp. 26-39, 2017.
  • [5] M. Fischer, M. Rybnicek, and S. Tjoa, "A novel palm vein recognition approach based on enhanced local Gabor binary patterns histogram sequence," in 2012 19th International Conference on Systems, Signals and Image Processing (IWSSIP), 2012: IEEE, pp. 429-432.
  • [6] C. Wilson, Vein pattern recognition: a privacy-enhancing biometric. CRC press, 2010.
  • [7] W. Shu and D. Zhang, "Automated personal identification by palmprint," Optical Engineering, vol. 37, pp. 2359-2362, 1998.
  • [8] A. Michele, V. Colin, and D. D. J. P. C. S. Santika, "Mobilenet convolutional neural networks and support vector machines for palmprint recognition," vol. 157, pp. 110-117, 2019.
  • [9] X.-Q. Wu, K.-Q. Wang, and D. Zhang, "Wavelet based palm print recognition," in Proceedings. International Conference on Machine Learning and Cybernetics, 2002, vol. 3: IEEE, pp. 1253-1257.
  • [10] S. F. ABD RAZAK, "IMAGE ANALYSIS OF PALM (PALMISTRY)(HEALTH AND CHARACTERISTICS)," BACHELOR OF ENGINEERING, ELECTRICAL & ELECTRONICS ENGINEERING, Universiti Teknologi PETRONAS, TRONOH, PERAK, 2006.
  • [11] J. Saint-Germain, Lovers Guide to Palmistry: Finding Love in the Palm of Your Hand. Llewellyn Worldwide, 2008.
  • [12] M. M. Houck and J. A. Siegel, Fundamentals of forensic science. Academic Press, 2009.
  • [13] A. R. Jackson and J. M. Jackson, Forensic science. Pearson Education, 2008.
  • [14] A. Younesi and M. C. J. P. C. S. Amirani, "Gabor filter and texture based features for palmprint recognition," vol. 108, pp. 2488-2495, 2017.
  • [15] A. K. Jain, A. Ross, and S. Prabhakar, "An introduction to biometric recognition," IEEE Transactions on circuits and systems for video technology, vol. 14, no. 1, pp. 4-20, 2004.
  • [16] Y. Bulatov, S. Jambawalikar, P. Kumar, and S. Sethia, "Hand recognition using geometric classifiers," in International Conference on Biometric Authentication, 2004: Springer, pp. 753-759.
  • [17] L. Fei, B. Zhang, W. Zhang, and S. J. I. S. Teng, "Local apparent and latent direction extraction for palmprint recognition," vol. 473, pp. 59-72, 2019.
  • [18] J. Cui, "Multispectral fusion for palmprint recognition," Optik-International Journal for Light, vol. 124, no. 17, pp. 3067-3071, 2013.
  • [19] S. Zhang and X. Gu, "Palmprint recognition method based on score level fusion," Optik-International Journal for Light and Electron Optics, vol. 124, no. 18, pp. 3340-3344, 2013.
  • [20] S. Zhang and X. Gu, "Palmprint recognition based on the representation in the feature space," vol. 124, no. 22, pp. 5434-5439, 2013.
  • [21] J. Li, J. Cao, and K. Lu, "Improve the two-phase test samples representation method for palmprint recognition," Optik, vol. 124, no. 24, pp. 6651-6656, 2013.
  • [22] S. Zhao and B. Zhang, "Deep discriminative representation for generic palmprint recognition," Pattern Recognition, vol. 98, p. 107071, 2020.
  • [23] W. M. Matkowski, T. Chai, and A. W. K. Kong, "Palmprint recognition in uncontrolled and uncooperative environment," IEEE Transactions on Information Forensics Security, vol. 15, pp. 1601-1615, 2019.
  • [24] Z. Liu, Y. Yin, H. Wang, S. Song, and Q. Li, "Finger vein recognition with manifold learning," Journal of Network Computer Applications, vol. 33, no. 3, pp. 275-282, 2010.
  • [25] D. Hong, W. Liu, J. Su, Z. Pan, and G. Wang, "A novel hierarchical approach for multispectral palmprint recognition," Neurocomputing, vol. 151, pp. 511-521, 2015.
  • [26] M. D. Bounneche, L. Boubchir, A. Bouridane, B. Nekhoul, and A. Ali-Chérif, "Multi-spectral palmprint recognition based on oriented multiscale log-Gabor filters," Neurocomputing, vol. 205, pp. 274-286, 2016.
  • [27] F. Ma, X. Zhu, C. Wang, H. Liu, and X.-Y. Jing, "Multi-orientation and multi-scale features discriminant learning for palmprint recognition," Neurocomputing, vol. 348, pp. 169-178, 2019.
  • [28] D. Tamrakar, P. J. J. o. V. C. Khanna, and I. Representation, "Kernel discriminant analysis of Block-wise Gaussian Derivative Phase Pattern Histogram for palmprint recognition," vol. 40, pp. 432-448, 2016.
  • [29] D. Hong, W. Liu, X. Wu, Z. Pan, and J. Su, "Robust palmprint recognition based on the fast variation Vese–Osher model," Neurocomputing, vol. 174, pp. 999-1012, 2016.
  • [30] C. Turan and K.-M. Lam, "Histogram-based local descriptors for facial expression recognition (FER): A comprehensive study," Journal of visual communication and image representation, vol. 55, pp. 331-341, 2018.
  • [31] T. TUNCER and A. J. T. B. V. B. B. v. M. D. Engin, "Yerel İkili Örüntü Tabanli Veri Gizleme Algoritmasi: LBP-LSB," vol. 10, no. 1, pp. 48-53, 2017.
  • [32] L. Liu, S. Lao, P. W. Fieguth, Y. Guo, X. Wang, and M. J. I. T. o. I. P. Pietikäinen, "Median robust extended local binary pattern for texture classification," vol. 25, no. 3, pp. 1368-1381, 2016.
  • [33] H. Wan, L. Chen, H. Song, and J. Yang, "Dorsal hand vein recognition based on convolutional neural networks," in 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2017: IEEE, pp. 1215-1221.
  • [34] M. Afifi, "11K Hands: Gender recognition and biometric identification using a large dataset of hand images," Multimedia Tools and Applications, vol. 78, no. 15, pp. 20835-20854, 2019.
  • [35] N. A. Al-johania and L. A. Elrefaei, "Dorsal hand vein recognition by convolutional neural networks: Feature learning and transfer learning approaches," International Journal of Intelligent Engineering and Systems, vol. 12, no. 3, pp. 178-91, 2019.
  • [36] S.-J. Chuang, "Vein recognition based on minutiae features in the dorsal venous network of the hand," Signal, Image and Video Processing, vol. 12, no. 3, pp. 573-581, 2018.
  • [37] S. A. Radzi, M. K. Hani, and R. Bakhteri, "Finger-vein biometric identification using convolutional neural network," Turkish Journal of Electrical Engineering & Computer Sciences, vol. 24, no. 3, pp. 1863-1878, 2016.
  • [38] L. Wang, G. Leedham, and S.-Y. Cho, "Infrared imaging of hand vein patterns for biometric purposes," IET computer vision, vol. 1, no. 3-4, pp. 113-122, 2007.
  • [39] L. Wang, G. Leedham, and D. S.-Y. Cho, "Minutiae feature analysis for infrared hand vein pattern biometrics," Pattern recognition, vol. 41, no. 3, pp. 920-929, 2008.
  • [40] O. O. Iroanya, T. F. Egwuatu, O. T. Talabi, and İ. S. Ogunleye. “Sex Prediction Using Finger, Hand and Foot Measurements for Forensic Identification in a Nigerian Population”. Sakarya University Journal of Science, vol. 24 no. 3, pp. 432-445, 2020.
There are 40 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence
Journal Section Araştırma Articlessi
Authors

Emrah Aydemir 0000-0002-8380-7891

Raghad Tohmas Esfandıyar Alalawı 0000-0002-6598-7251

Publication Date January 30, 2023
Published in Issue Year 2023

Cite

APA Aydemir, E., & Esfandıyar Alalawı, R. T. (2023). Classification Of Hand Images by Person, Age and Gender with The Median Robust Extended Local Binary Model. Balkan Journal of Electrical and Computer Engineering, 11(1), 78-87. https://doi.org/10.17694/bajece.1171905

All articles published by BAJECE are licensed under the Creative Commons Attribution 4.0 International License. This permits anyone to copy, redistribute, remix, transmit and adapt the work provided the original work and source is appropriately cited.Creative Commons Lisansı