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Classification of foot images according to person, age, and gender with the local phase quantization

Yıl 2022, Cilt: 11 Sayı: 3, 467 - 476, 18.07.2022
https://doi.org/10.28948/ngumuh.1055199

Öz

Foot images are an important biological feature of the human body and carry various characteristics of people. The texture in the footprint, shape, length, etc. It may be possible to identify a person by looking at different qualities. Although the hand structure has its own unique shape and skin texture, comparison of these with foot biometrics reveals a complex situation. The main reasons for this include close toes, the absence of typical lines in footprints, and the high noise content of turned footprints. However, although these details are not similar to the hand, they cause differences in foot images for each person. In addition to these, foot images also differ according to age, gender, race, shoes and age of starting to wear shoes. In this study, 6944 data, which are right and left foot images of 100 people, were collected. The features of these collected files were extracted by local phase quantization. For each image file, 1x256 vectors were produced. These processes were performed for all files and images were classified for person, age and gender with many different classification algorithms. While 99.42% accuracy rate was obtained for person recognition, 99.87% success was achieved for gender. Finally, 98.14% classification success was achieved for age. All these results show that recognition from foot images is possible with high success with the method here.

Kaynakça

  • D. Ashlock and J. Davidson, Lexicodes in the space of foot patterns for image classification. in 1998 IEEE Southwest Symposium on Image Analysis and Interpretation (Cat. No. 98EX165), IEEE, pp. 97-102, 1998.
  • K. M. Hashem and F. Ghali, Human identification using foot features. Int J Eng Manuf, 6, (4), 22-31, 2016. https://doi.org/10.5815/ijem.2016.04.03.
  • W. Jia, H.-Y. Cai, J. Gui, R.-X. Hu, Y.-K. Lei, and X.-F. Wang, Newborn footprint recognition using orientation feature. Neural Computing and Applications, 21, (8), 1855-1863, 2012. https://doi.org/10.1007/s00521-011-0530-9.
  • E. Liu, Infant footprint recognition. in Proceedings of the IEEE International Conference on Computer Vision, pp. 1653-1660, 2017.
  • J. Jaruenpunyasak and R. Duangsoithong, Empirical analysis of feature reduction in deep learning and conventional methods for foot image classification. IEEE Access, 9, 53133-53145, 2021. https://doi.org/10.1109/ACCESS.2021.3069625.
  • R. Ghotaslou, M. Y. Memar, and N. Alizadeh, Classification, microbiology and treatment of diabetic foot infections. Journal of wound care, 27, (7), 434-441, 2018. https://doi.org/ 10.12968/jowc.2018.27.7.434.
  • R. Khokher, R. C. Singh, and R. Kumar, Footprint recognition with principal component analysis and independent component analysis. in Macromolecular symposia, 347, (1), pp. 16-26, 2015. https://doi.org/10.1002/masy.201400045.
  • S. F. Stewart, Human gait and the human foot: an ethnological study of flatfoot: Part I. Clinical Orthopaedics and Related Research (1976-2007), 70, 111-123, 1970.
  • J. B. Volpon, Footprint analysis during the growth period. Journal of pediatric orthopedics, 14, (1), 83-85, 1994. https://doi.org/10.1097/01241398-199401000-00017.
  • S. Braun, L. Basquin, and C. Mery, The contour of the normal foot. A statistical study. Revue du Rhumatisme et des Maladies Ostéo-articulaires, 47, (2), 127-133, 1980.
  • U. B. Rao and B. Joseph, The influence of footwear on the prevalence of flat foot. A survey of 2300 children. The Journal of bone and joint surgery. British volume, 74, (4), 525-527, 1992. https://doi.org/10.1302/0301-620x.74b4.1624509.
  • Y. Park, J. Lee, and K. Park, Foot shape classification methods based on image processing for shoe manufacturing. in 2019 International Conference on Information and Communication Technology Convergence (ICTC), IEEE, 1265-1268, 2019: https://doi.org/10.1109/ICTC46691.2019.8939978.
  • D.-H. Seong, U.-S. Jeong, and Y.-J. Jo, A study on the categorization of korean foot shapes. Journal of the Ergonomics Society of Korea, 25, (2), 107-118, 2006. https://doi.org/10.5143/JESK.2006.25.2.107.
  • S.-H. Choi and J.-S. Chun, The comparison of foot shape classification methods. The Research Journal of the Costume Culture, 15, (2), 252-264, 2007.
  • A. F. Ola, Personality Identification System based on Human Foot Anatomy. International Journal of Science and Research (IJSR), 9, (2), 490-497, 2018. https://doi.org/10.21275/ART20201838.
  • R. Robinson. Foot Reading. Pedi Reviews Foot care expert. https://www.pedireviews.co.uk/foot-reading/ (accessed 20.09.2021.
  • G. Luo, V. L. Houston, M. Mussman, M. Garbarini, A. C. Beattie, and C. Thongpop, Comparison of male and female foot shape. Journal of the American Podiatric Medical Association, 99, (5), 383-390, 2009. https://doi.org/10.7547%2F0990383.
  • K. A. Reinker and S. Ozburne, A comparison of male and female orthopaedic pathology in basic training. Military medicine, 144, (8), 532-536, 1979. https://doi.org/10.1093/milmed/144.8.532.
  • L. Rosendal, H. Langberg, A. Skov-Jensen, and M. Kjær, Incidence of injury and physical performance adaptations during military training. Clinical Journal of Sport Medicine, 13, (3), 157-163, 2003.
  • F. P. Oliveira, A. Sousa, R. Santos, and J. M. R. Tavares, Towards an efficient and robust foot classification from pedobarographic images. Computer methods in biomechanics and biomedical engineering, 15, (11), 1181-1188, 2012. https://doi.org/ 10.1080/10255842.2011.581239.
  • F. P. Oliveira, T. C. Pataky, and J. M. R. Tavares, Registration of pedobarographic image data in the frequency domain. Computer methods in biomechanics and biomedical engineering, 13, (6), 731-740, 2010. https://doi.org/10.1080/10255840903573020.
  • Z. Q. Liang, Y. Meng, S. Popik, and F. F. Chen, Analysis of foot morphology in habitually barefoot group. Journal of Biomimetics, Biomaterials and Biomedical Engineering, 41, 1-9, 2019. https://doi.org/10.4028/www.scientific.net/JBBBE.41.1.
  • T. C. Michaud, Human locomotion: the conservative management of gait-related disorders. Newton Biomechanics, 2011.
  • Y. Hong, L. Wang, D. Q. Xu, and J. X. Li, Gender differences in foot shape: a study of Chinese young adults. Sports biomechanics, 10, (02), 85-97, 2011. https://doi.org/10.1080/14763141.2011.569567.
  • M. Saghazadeh, N. Kitano, and T. Okura, Gender differences of foot characteristics in older Japanese adults using a 3D foot scanner. Journal of foot and ankle research, 8, (1), 1-7, 2015. https://doi.org/ 10.1186/s13047-015-0087-4.
  • V. Ojansivu and J. Heikkilä, Blur insensitive texture classification using local phase quantization. in International conference on image and signal processing: Springer, pp. 236-243, 2008.
  • T. Ahonen, E. Rahtu, V. Ojansivu, and J. Heikkila, Recognition of blurred faces using local phase quantization. in 2008 19th international conference on pattern recognition: IEEE, pp. 1-4, 2008.
  • A. Durmuşoğlu and Y. Kahraman, Face Expression Recognition Using a Combination of Local Binary Patterns and Local Phase Quantization. in 2021 International Conference on Communication, Control and Information Sciences (ICCISc), 1: IEEE, pp. 1-5, 2021.
  • H.-T. Nguyen, Contributions to facial feature extraction for face recognition. Université de Grenoble, 2014.
  • T. Keatsamarn and C. Pintavirooj, Footprint Identification using Deep Learning. in 2018 11th Biomedical Engineering International Conference (BMEiCON): IEEE, pp. 1-4, 2018.
  • W. Bao, Y. Wang, N. Wang, and J. Tang, Optical Footprint Image Recognition Algorithm Based on Metric Learning and SVM. in 2020 International Conference on Computer Engineering and Application (ICCEA): IEEE, pp. 864-868, 2020.
  • R. Wang, W. Hong, and N. Yang, The research on footprint recognition method based on wavelet and fuzzy neural network. in 2009 Ninth International Conference on Hybrid Intelligent Systems, 3, 428-432, 2009. https://doi.org/10.1109/HIS.2009.300.
  • M. H. Yap et al., A new mobile application for standardizing diabetic foot images. Journal of diabetes science and technology, 12, (1), 169-173, 2018. https://doi.org/10.1177/1932296817713761.
  • J. Chae, Y.-J. Kang, and Y. Noh, A deep-learning approach for foot-type classification using heterogeneous pressure data. Sensors, 20, (16), 4481, 2020. https://doi.org/10.3390/s20164481

Yerel faz niceleme ile ayak görüntülerinin kişi, yaş ve cinsiyete göre sınıflandırılması

Yıl 2022, Cilt: 11 Sayı: 3, 467 - 476, 18.07.2022
https://doi.org/10.28948/ngumuh.1055199

Öz

Ayak görüntüleri insan vücudunun önemli bir biyolojik özelliği olup insanların çeşitli özelliklerini taşır. Ayak izindeki doku, şekil, uzunluk vb. farklı niteliklere bakılarak kişi tanımlamak mümkün olabilir. El yapısı kendine özgü şekil ve cilt dokusuna sahip olsa da bunların ayak biyometrisi ile karşılaştırılması karmaşık bir durum ortaya çıkarmaktadır. Bunun temel nedenleri arasında yakın ayak parmakları, ayak izlerindeki tipik çizgilerin yokluğu ve dönmüş ayak izlerinin yüksek gürültü içermesi yer almaktadır. Fakat bu ayrıntılar el ile benzerlik göstermese de her kişi için ayak görüntülerinde farklılık oluşmasına neden olmaktadır. Bunların yanı sıra ayak görüntüleri yaş, cinsiyet, ırk, ayakkabılar ve ayakkabı giymeye başlama yaşına göre de farklılık göstermektedir. Buradaki çalışma da 100 kişiye ait sağ ve sol ayak görüntüleri olan 6944 veri toplanmıştır. Toplanan bu dosyaların yerel faz niceleme ile öznitelikleri çıkarılmıştır. Her bir görüntü dosyası için 1x256 boyutlarında vektör üretilmiştir. Tüm dosyalar için bu işlemler yapılmış ve birçok farklı sınıflandırma algoritmaları ile görüntüler kişi, yaş ve cinsiyet için sınıflandırılmıştır. Kişi tanıma için % 99.42 oranında doğruluk oranı elde edilirken, cinsiyet için % 99.87 oranında başarı elde edilmiştir. Son olarak yaş için ise % 98.14 oranında sınıflandırma başarısına ulaşılmıştır.

Kaynakça

  • D. Ashlock and J. Davidson, Lexicodes in the space of foot patterns for image classification. in 1998 IEEE Southwest Symposium on Image Analysis and Interpretation (Cat. No. 98EX165), IEEE, pp. 97-102, 1998.
  • K. M. Hashem and F. Ghali, Human identification using foot features. Int J Eng Manuf, 6, (4), 22-31, 2016. https://doi.org/10.5815/ijem.2016.04.03.
  • W. Jia, H.-Y. Cai, J. Gui, R.-X. Hu, Y.-K. Lei, and X.-F. Wang, Newborn footprint recognition using orientation feature. Neural Computing and Applications, 21, (8), 1855-1863, 2012. https://doi.org/10.1007/s00521-011-0530-9.
  • E. Liu, Infant footprint recognition. in Proceedings of the IEEE International Conference on Computer Vision, pp. 1653-1660, 2017.
  • J. Jaruenpunyasak and R. Duangsoithong, Empirical analysis of feature reduction in deep learning and conventional methods for foot image classification. IEEE Access, 9, 53133-53145, 2021. https://doi.org/10.1109/ACCESS.2021.3069625.
  • R. Ghotaslou, M. Y. Memar, and N. Alizadeh, Classification, microbiology and treatment of diabetic foot infections. Journal of wound care, 27, (7), 434-441, 2018. https://doi.org/ 10.12968/jowc.2018.27.7.434.
  • R. Khokher, R. C. Singh, and R. Kumar, Footprint recognition with principal component analysis and independent component analysis. in Macromolecular symposia, 347, (1), pp. 16-26, 2015. https://doi.org/10.1002/masy.201400045.
  • S. F. Stewart, Human gait and the human foot: an ethnological study of flatfoot: Part I. Clinical Orthopaedics and Related Research (1976-2007), 70, 111-123, 1970.
  • J. B. Volpon, Footprint analysis during the growth period. Journal of pediatric orthopedics, 14, (1), 83-85, 1994. https://doi.org/10.1097/01241398-199401000-00017.
  • S. Braun, L. Basquin, and C. Mery, The contour of the normal foot. A statistical study. Revue du Rhumatisme et des Maladies Ostéo-articulaires, 47, (2), 127-133, 1980.
  • U. B. Rao and B. Joseph, The influence of footwear on the prevalence of flat foot. A survey of 2300 children. The Journal of bone and joint surgery. British volume, 74, (4), 525-527, 1992. https://doi.org/10.1302/0301-620x.74b4.1624509.
  • Y. Park, J. Lee, and K. Park, Foot shape classification methods based on image processing for shoe manufacturing. in 2019 International Conference on Information and Communication Technology Convergence (ICTC), IEEE, 1265-1268, 2019: https://doi.org/10.1109/ICTC46691.2019.8939978.
  • D.-H. Seong, U.-S. Jeong, and Y.-J. Jo, A study on the categorization of korean foot shapes. Journal of the Ergonomics Society of Korea, 25, (2), 107-118, 2006. https://doi.org/10.5143/JESK.2006.25.2.107.
  • S.-H. Choi and J.-S. Chun, The comparison of foot shape classification methods. The Research Journal of the Costume Culture, 15, (2), 252-264, 2007.
  • A. F. Ola, Personality Identification System based on Human Foot Anatomy. International Journal of Science and Research (IJSR), 9, (2), 490-497, 2018. https://doi.org/10.21275/ART20201838.
  • R. Robinson. Foot Reading. Pedi Reviews Foot care expert. https://www.pedireviews.co.uk/foot-reading/ (accessed 20.09.2021.
  • G. Luo, V. L. Houston, M. Mussman, M. Garbarini, A. C. Beattie, and C. Thongpop, Comparison of male and female foot shape. Journal of the American Podiatric Medical Association, 99, (5), 383-390, 2009. https://doi.org/10.7547%2F0990383.
  • K. A. Reinker and S. Ozburne, A comparison of male and female orthopaedic pathology in basic training. Military medicine, 144, (8), 532-536, 1979. https://doi.org/10.1093/milmed/144.8.532.
  • L. Rosendal, H. Langberg, A. Skov-Jensen, and M. Kjær, Incidence of injury and physical performance adaptations during military training. Clinical Journal of Sport Medicine, 13, (3), 157-163, 2003.
  • F. P. Oliveira, A. Sousa, R. Santos, and J. M. R. Tavares, Towards an efficient and robust foot classification from pedobarographic images. Computer methods in biomechanics and biomedical engineering, 15, (11), 1181-1188, 2012. https://doi.org/ 10.1080/10255842.2011.581239.
  • F. P. Oliveira, T. C. Pataky, and J. M. R. Tavares, Registration of pedobarographic image data in the frequency domain. Computer methods in biomechanics and biomedical engineering, 13, (6), 731-740, 2010. https://doi.org/10.1080/10255840903573020.
  • Z. Q. Liang, Y. Meng, S. Popik, and F. F. Chen, Analysis of foot morphology in habitually barefoot group. Journal of Biomimetics, Biomaterials and Biomedical Engineering, 41, 1-9, 2019. https://doi.org/10.4028/www.scientific.net/JBBBE.41.1.
  • T. C. Michaud, Human locomotion: the conservative management of gait-related disorders. Newton Biomechanics, 2011.
  • Y. Hong, L. Wang, D. Q. Xu, and J. X. Li, Gender differences in foot shape: a study of Chinese young adults. Sports biomechanics, 10, (02), 85-97, 2011. https://doi.org/10.1080/14763141.2011.569567.
  • M. Saghazadeh, N. Kitano, and T. Okura, Gender differences of foot characteristics in older Japanese adults using a 3D foot scanner. Journal of foot and ankle research, 8, (1), 1-7, 2015. https://doi.org/ 10.1186/s13047-015-0087-4.
  • V. Ojansivu and J. Heikkilä, Blur insensitive texture classification using local phase quantization. in International conference on image and signal processing: Springer, pp. 236-243, 2008.
  • T. Ahonen, E. Rahtu, V. Ojansivu, and J. Heikkila, Recognition of blurred faces using local phase quantization. in 2008 19th international conference on pattern recognition: IEEE, pp. 1-4, 2008.
  • A. Durmuşoğlu and Y. Kahraman, Face Expression Recognition Using a Combination of Local Binary Patterns and Local Phase Quantization. in 2021 International Conference on Communication, Control and Information Sciences (ICCISc), 1: IEEE, pp. 1-5, 2021.
  • H.-T. Nguyen, Contributions to facial feature extraction for face recognition. Université de Grenoble, 2014.
  • T. Keatsamarn and C. Pintavirooj, Footprint Identification using Deep Learning. in 2018 11th Biomedical Engineering International Conference (BMEiCON): IEEE, pp. 1-4, 2018.
  • W. Bao, Y. Wang, N. Wang, and J. Tang, Optical Footprint Image Recognition Algorithm Based on Metric Learning and SVM. in 2020 International Conference on Computer Engineering and Application (ICCEA): IEEE, pp. 864-868, 2020.
  • R. Wang, W. Hong, and N. Yang, The research on footprint recognition method based on wavelet and fuzzy neural network. in 2009 Ninth International Conference on Hybrid Intelligent Systems, 3, 428-432, 2009. https://doi.org/10.1109/HIS.2009.300.
  • M. H. Yap et al., A new mobile application for standardizing diabetic foot images. Journal of diabetes science and technology, 12, (1), 169-173, 2018. https://doi.org/10.1177/1932296817713761.
  • J. Chae, Y.-J. Kang, and Y. Noh, A deep-learning approach for foot-type classification using heterogeneous pressure data. Sensors, 20, (16), 4481, 2020. https://doi.org/10.3390/s20164481
Toplam 34 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Bilgisayar Yazılımı
Bölüm Bilgisayar Mühendisliği
Yazarlar

Emrah Aydemir 0000-0002-8380-7891

Mustafa Shwaish Hameed Al-azzawi 0000-0002-0070-699X

Yayımlanma Tarihi 18 Temmuz 2022
Gönderilme Tarihi 8 Ocak 2022
Kabul Tarihi 12 Nisan 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 11 Sayı: 3

Kaynak Göster

APA Aydemir, E., & Hameed Al-azzawi, M. S. (2022). Yerel faz niceleme ile ayak görüntülerinin kişi, yaş ve cinsiyete göre sınıflandırılması. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 11(3), 467-476. https://doi.org/10.28948/ngumuh.1055199
AMA Aydemir E, Hameed Al-azzawi MS. Yerel faz niceleme ile ayak görüntülerinin kişi, yaş ve cinsiyete göre sınıflandırılması. NÖHÜ Müh. Bilim. Derg. Temmuz 2022;11(3):467-476. doi:10.28948/ngumuh.1055199
Chicago Aydemir, Emrah, ve Mustafa Shwaish Hameed Al-azzawi. “Yerel Faz Niceleme Ile Ayak görüntülerinin kişi, Yaş Ve Cinsiyete göre sınıflandırılması”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 11, sy. 3 (Temmuz 2022): 467-76. https://doi.org/10.28948/ngumuh.1055199.
EndNote Aydemir E, Hameed Al-azzawi MS (01 Temmuz 2022) Yerel faz niceleme ile ayak görüntülerinin kişi, yaş ve cinsiyete göre sınıflandırılması. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 11 3 467–476.
IEEE E. Aydemir ve M. S. Hameed Al-azzawi, “Yerel faz niceleme ile ayak görüntülerinin kişi, yaş ve cinsiyete göre sınıflandırılması”, NÖHÜ Müh. Bilim. Derg., c. 11, sy. 3, ss. 467–476, 2022, doi: 10.28948/ngumuh.1055199.
ISNAD Aydemir, Emrah - Hameed Al-azzawi, Mustafa Shwaish. “Yerel Faz Niceleme Ile Ayak görüntülerinin kişi, Yaş Ve Cinsiyete göre sınıflandırılması”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 11/3 (Temmuz 2022), 467-476. https://doi.org/10.28948/ngumuh.1055199.
JAMA Aydemir E, Hameed Al-azzawi MS. Yerel faz niceleme ile ayak görüntülerinin kişi, yaş ve cinsiyete göre sınıflandırılması. NÖHÜ Müh. Bilim. Derg. 2022;11:467–476.
MLA Aydemir, Emrah ve Mustafa Shwaish Hameed Al-azzawi. “Yerel Faz Niceleme Ile Ayak görüntülerinin kişi, Yaş Ve Cinsiyete göre sınıflandırılması”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, c. 11, sy. 3, 2022, ss. 467-76, doi:10.28948/ngumuh.1055199.
Vancouver Aydemir E, Hameed Al-azzawi MS. Yerel faz niceleme ile ayak görüntülerinin kişi, yaş ve cinsiyete göre sınıflandırılması. NÖHÜ Müh. Bilim. Derg. 2022;11(3):467-76.

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