Sensör işaretlerinden cinsiyet tanıma için yerel ikili örüntüler tabanlı yeni yaklaşımlar

Fatma Kuncan; Yılmaz Kaya; Melih Kuncan

doi:10.17341/gazimmfd.426259

Research Article

Sensör işaretlerinden cinsiyet tanıma için yerel ikili örüntüler tabanlı yeni yaklaşımlar

Year 2019, Volume: 34 Issue: 4, 2173 - 2186, 25.06.2019

Fatma Kuncan Yılmaz Kaya Melih Kuncan

https://doi.org/10.17341/gazimmfd.426259

Cited By: 18

Abstract

Son
zamanlarda kimlik tanıma başta olmak üzere, yaş tanıma ve cinsiyet tanıma gibi
konular hem akademik hem de diğer alanlarda (sanayi, bilişim, sağlık vb.) yaygın
olarak üzerinde araştırma yapılan konulardandır. Cinsiyet tanıma (CT) erkek ve
kadın arasında ayrım yapan özelliklere dayalı olarak bireyin cinsiyetini
belirlemektir. Yapay zeka alanında, CT örüntü tanıma yönteminin en önemli
uygulamalarından biri olarak kabul edilmektedir. Bu çalışmada kişilerin 5
farklı bölgesine takılmış olan ivmeölçer, manyetometre ve jiroskop
sensörlerinden elde edilen işaretler kullanılarak cinsiyet tanıma (CT) için üç
(3) farklı öznitelik çıkarım metodu önerilmiştir. İşaretlerden öznitelik
çıkarımı CT’nın en önemli aşamalarından biridir. Çünkü CT’nin başarısı
çıkarılan özniteliklere bağlıdır. Ancak CT için uygun özniteliklerin çıkarım
zor bir problemdir. Sensörlerden elde edilen işaretlere Bir Boyutlu Yerel İkili
Örüntüler (1B-YİÖ), Bir Boyutlu Sağlam Yerel İkili Örüntüler (1B-SYİÖ) ve
Ağırlıklandırılmış Bir Boyutlu Sağlam Yerel İkili Örüntüler (A-1B-SYİÖ) olmak
üzere farklı dönüşüm yöntemleri uygulanmıştır. Dönüşüm işlemlerinden sonra yeni
oluşan işaretlerde istatistiksel öznitelikler elde edilmiştir. Bu öznitelikler
kullanılarak farklı makine öğrenmesi yöntemler (SVM, RF, YSA, Knn) ile
sınıflandırma işlemleri gerçekleştirilmiştir. Elde edilen sonuçlara göre 1B-YİÖ
(%96.04), 1B-SYİÖ (%96.72) ve A-1B-SYİÖ (%97.28) yöntemlerin CT için etkin
öznitelikler sağladığı görülmüştür. Bu çalışmada önerilen yeni yaklaşımlar
sayesinde sensör işaretleri kullanılarak CT işleminin yüksek bir başarı oranı
ile gerçekleştirildiği belirlenmiştir.

Keywords

Cinsiyet Tanıma, sensör işaretleri, 1B-YİÖ, 1B-SYİÖ, A-1B-SYİÖ, öznitelik çıkarımı

References

1. Cao, L., Dikmen, M., Fu, Y., & Huang, T. S. (2008, October). Gender recognition from body. In Proceedings of the 16th ACM international conference on Multimedia (pp. 725-728). ACM.
2. Yu, S., Tan, T., Huang, K., Jia, K., & Wu, X. (2009). A study on gait-based gender classification. IEEE Transactions on image processing, 18(8), 1905-1910.
3. Golomb, B. A., Lawrence, D. T., & Sejnowski, T. J. (1990, October). Sexnet: A neural network identifies sex from human faces. In NIPS (Vol. 1, p. 2).
4. Harb, H., & Chen, L. (2003, July). Gender identification using a general audio classifier. In Multimedia and Expo, 2003. ICME'03. Proceedings. 2003 International Conference on (Vol. 2, pp. II-733). IEEE.
5. Li, X., Maybank, S. J., Yan, S., Tao, D., & Xu, D. (2008). Gait components and their application to gender recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38(2), 145-155.
6. Guo, G., Mu, G., & Fu, Y. (2009, September). Gender from body: A biologically-inspired approach with manifold learning. In Asian Conference on Computer Vision (pp. 236-245). Springer, Berlin, Heidelberg.
7. Gutta, S., Huang, J. R., Jonathon, P., & Wechsler, H. (2000). Mixture of experts for classification of gender, ethnic origin, and pose of human faces. IEEE Transactions on neural networks, 11(4), 948-960.
8. Moghaddam, B., & Yang, M. H. (2002). Learning gender with support faces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5), 707-711.
9. Li, Z., Zhou, X., & Huang, T. S. (2009, November). Spatial gaussian mixture model for gender recognition. In Image Processing (ICIP), 2009 16th IEEE International Conference on (pp. 45-48). IEEE.
10. Chen, C., & Ross, A. (2011, October). Evaluation of gender classification methods on thermal and near-infrared face images. In Biometrics (IJCB), 2011 International Joint Conference on (pp. 1-8). IEEE.
11. Ng, C. B., Tay, Y. H., & Goi, B. M. (2012). Vision-based human gender recognition. A survey. arXiv preprint arXiv. 1204.1611.
12. Danisman, T., Bilasco, I. M., & Martinet, J. (2015). Boosting gender recognition performance with a fuzzy inference system. Expert Systems with Applications, 42(5), 2772-2784.
13. Li, X., Maybank, S. J., Yan, S., Tao, D., & Xu, D. (2008). Gait components and their application to gender recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38(2), 145-155.
14. Lu, J., Wang, G., & Moulin, P. (2014). Human identity and gender recognition from gait sequences with arbitrary walking directions. IEEE Transactions on Information Forensics and Security, 9(1), 51-61.
15. Igual, L., Lapedriza, À., & Borràs, R. (2013). Robust gait-based gender classification using depth cameras. EURASIP Journal on Image and Video Processing, 2013(1), 1.
16. Gnanasivam, P., & Muttan, S. (2013). Gender classification using ear biometrics. In Proceedings of the Fourth International Conference on Signal and Image Processing 2012 (ICSIP 2012) (pp. 137-148). Springer, India.
17. Li, X., Zhao, X., Fu, Y., & Liu, Y. (2010, June). Bimodal gender recognition from face and fingerprint. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on (pp. 2590-2597). IEEE.
18. Wu, M., & Yuan, Y. (2014). Gender classification based on geometry features of palm image. The Scientific World Journal, 2014.
19. Thomas, V., Chawla, N. V., Bowyer, K. W., & Flynn, P. J. (2007, September). Learning to predict gender from iris images. In Biometrics: Theory, Applications, and Systems, 2007. BTAS 2007. First IEEE International Conference on (pp. 1-5). IEEE.
20. Li, X., Zhao, X., Fu, Y., & Liu, Y. (2010, June). Bimodal gender recognition from face and fingerprint. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on (pp. 2590-2597). IEEE
21. Shan, C., Gong, S., & McOwan, P. W. (2008). Fusing gait and face cues for human gender recognition. Neurocomputing, 71(10-12), 1931-1938.
22. Zhang, D., & Wang, Y. (2009, June). Gender recognition based on fusion of face and multi-view gait. In International Conference on Biometrics (pp. 1010-1018). Springer, Berlin, Heidelberg.
23. Jain, A. K., Nandakumar, K., Lu, X., & Park, U. (2004, May). Integrating faces, fingerprints, and soft biometric traits for user recognition. In International Workshop on Biometric Authentication (pp. 259-269). Springer, Berlin, Heidelberg.
24. Park, U., & Jain, A. K. (2010). Face matching and retrieval using soft biometrics. IEEE Transactions on Information Forensics and Security, 5(3), 406-415.
25. Thang, H. M., Viet, V. Q., Thuc, N. D., & Choi, D. (2012, November). Gait identification using accelerometer on mobile phone. In Control, Automation and Information Sciences (ICCAIS), 2012 International Conference on (pp. 344-348). IEEE.
26. Zhong, Y., & Deng, Y. (2014, September). Sensor orientation invariant mobile gait biometrics. In Biometrics (IJCB), 2014 IEEE International Joint Conference on (pp. 1-8). IEEE.
27. Thang, H. M., Viet, V. Q., Thuc, N. D., & Choi, D. (2012, November). Gait identification using accelerometer on mobile phone. In Control, Automation and Information Sciences (ICCAIS), 2012 International Conference on (pp. 344-348). IEEE.
28. Zhang, Y., Pan, G., Jia, K., Lu, M., Wang, Y., & Wu, Z. (2015). Accelerometer-based gait recognition by sparse representation of signature points with clusters. IEEE transactions on cybernetics, 45(9), 1864-1875.
29. Weiss, G. M., & Lockhart, J. W. (2011, August). Identifying user traits by mining smart phone accelerometer data. In Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data (pp. 61-69). ACM.
30. Jain, A., & Kanhangad, V. (2016, March). Investigating gender recognition in smartphones using accelerometer and gyroscope sensor readings. In Computational Techniques in Information and Communication Technologies (ICCTICT), 2016 International Conference on (pp. 597-602). IEEE.
31. Jain, A., & Kanhangad, V. (2018). Gender classification in smartphones using gait information. Expert Systems with Applications, 93, 257-266.
32. Seviş, K. N. (2017). Biometrics for smartphones: age recognition, gender recognition and idenfication (Doctoral dissertation).
33. Altun, K., Barshan, B., & Tunçel, O. (2010). Comparative study on classifying human activities with miniature inertial and magnetic sensors. Pattern Recognition, 43(10), 3605-3620.
34. Barshan, B., & Yüksek, M. C. (2014). Recognizing daily and sports activities in two open source machine learning environments using body-worn sensor units. The Computer Journal, 57(11), 1649-1667.
35. Altun, K., & Barshan, B. (2010, August). Human activity recognition using inertial/magnetic sensor units. In International Workshop on Human Behavior Understanding (pp. 38-51). Springer, Berlin, Heidelberg.
36. Kaya, Y., Uyar, M., Tekin, R., & Yıldırım, S. (2014). 1D-local binary pattern based feature extraction for classification of epileptic EEG signals. Applied Mathematics and Computation, 243, 209-219.
37. Zhao, Y., Jia, W., Hu, R. X., & Min, H. (2013). Completed robust local binary pattern for texture classification. Neurocomputing, 106, 68-76.
38. Avcı, E. (2007). Doku Tipi İmgelerin Sınıflandırılması İçin Bir Uyarlamalı Entropi Tabanlı Dalgacık-Yapay Sinir Ağı Sistemi. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 22(1).
39. Takçı, H., & Canbay, P. (2017). Kişisel verilerin korunmasında öznitelik tabanlı gizlilik etki değerlendirmesi yöntemi. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 32(4), 1301-1310.
40. Çelik, C., & Bilge, H. Ş. (2015). Ağırlıklandırılmış Koşullu Karşılıklı Bilgi İle Öznitelik Seçimi. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 30(4).

Year 2019, Volume: 34 Issue: 4, 2173 - 2186, 25.06.2019

Fatma Kuncan Yılmaz Kaya Melih Kuncan

https://doi.org/10.17341/gazimmfd.426259

Cited By: 18

Abstract

References

1. Cao, L., Dikmen, M., Fu, Y., & Huang, T. S. (2008, October). Gender recognition from body. In Proceedings of the 16th ACM international conference on Multimedia (pp. 725-728). ACM.
2. Yu, S., Tan, T., Huang, K., Jia, K., & Wu, X. (2009). A study on gait-based gender classification. IEEE Transactions on image processing, 18(8), 1905-1910.
3. Golomb, B. A., Lawrence, D. T., & Sejnowski, T. J. (1990, October). Sexnet: A neural network identifies sex from human faces. In NIPS (Vol. 1, p. 2).
4. Harb, H., & Chen, L. (2003, July). Gender identification using a general audio classifier. In Multimedia and Expo, 2003. ICME'03. Proceedings. 2003 International Conference on (Vol. 2, pp. II-733). IEEE.
5. Li, X., Maybank, S. J., Yan, S., Tao, D., & Xu, D. (2008). Gait components and their application to gender recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38(2), 145-155.
6. Guo, G., Mu, G., & Fu, Y. (2009, September). Gender from body: A biologically-inspired approach with manifold learning. In Asian Conference on Computer Vision (pp. 236-245). Springer, Berlin, Heidelberg.
7. Gutta, S., Huang, J. R., Jonathon, P., & Wechsler, H. (2000). Mixture of experts for classification of gender, ethnic origin, and pose of human faces. IEEE Transactions on neural networks, 11(4), 948-960.
8. Moghaddam, B., & Yang, M. H. (2002). Learning gender with support faces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5), 707-711.
9. Li, Z., Zhou, X., & Huang, T. S. (2009, November). Spatial gaussian mixture model for gender recognition. In Image Processing (ICIP), 2009 16th IEEE International Conference on (pp. 45-48). IEEE.
10. Chen, C., & Ross, A. (2011, October). Evaluation of gender classification methods on thermal and near-infrared face images. In Biometrics (IJCB), 2011 International Joint Conference on (pp. 1-8). IEEE.
11. Ng, C. B., Tay, Y. H., & Goi, B. M. (2012). Vision-based human gender recognition. A survey. arXiv preprint arXiv. 1204.1611.
12. Danisman, T., Bilasco, I. M., & Martinet, J. (2015). Boosting gender recognition performance with a fuzzy inference system. Expert Systems with Applications, 42(5), 2772-2784.
13. Li, X., Maybank, S. J., Yan, S., Tao, D., & Xu, D. (2008). Gait components and their application to gender recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38(2), 145-155.
14. Lu, J., Wang, G., & Moulin, P. (2014). Human identity and gender recognition from gait sequences with arbitrary walking directions. IEEE Transactions on Information Forensics and Security, 9(1), 51-61.
15. Igual, L., Lapedriza, À., & Borràs, R. (2013). Robust gait-based gender classification using depth cameras. EURASIP Journal on Image and Video Processing, 2013(1), 1.
16. Gnanasivam, P., & Muttan, S. (2013). Gender classification using ear biometrics. In Proceedings of the Fourth International Conference on Signal and Image Processing 2012 (ICSIP 2012) (pp. 137-148). Springer, India.
17. Li, X., Zhao, X., Fu, Y., & Liu, Y. (2010, June). Bimodal gender recognition from face and fingerprint. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on (pp. 2590-2597). IEEE.
18. Wu, M., & Yuan, Y. (2014). Gender classification based on geometry features of palm image. The Scientific World Journal, 2014.
19. Thomas, V., Chawla, N. V., Bowyer, K. W., & Flynn, P. J. (2007, September). Learning to predict gender from iris images. In Biometrics: Theory, Applications, and Systems, 2007. BTAS 2007. First IEEE International Conference on (pp. 1-5). IEEE.
20. Li, X., Zhao, X., Fu, Y., & Liu, Y. (2010, June). Bimodal gender recognition from face and fingerprint. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on (pp. 2590-2597). IEEE
21. Shan, C., Gong, S., & McOwan, P. W. (2008). Fusing gait and face cues for human gender recognition. Neurocomputing, 71(10-12), 1931-1938.
22. Zhang, D., & Wang, Y. (2009, June). Gender recognition based on fusion of face and multi-view gait. In International Conference on Biometrics (pp. 1010-1018). Springer, Berlin, Heidelberg.
23. Jain, A. K., Nandakumar, K., Lu, X., & Park, U. (2004, May). Integrating faces, fingerprints, and soft biometric traits for user recognition. In International Workshop on Biometric Authentication (pp. 259-269). Springer, Berlin, Heidelberg.
24. Park, U., & Jain, A. K. (2010). Face matching and retrieval using soft biometrics. IEEE Transactions on Information Forensics and Security, 5(3), 406-415.
25. Thang, H. M., Viet, V. Q., Thuc, N. D., & Choi, D. (2012, November). Gait identification using accelerometer on mobile phone. In Control, Automation and Information Sciences (ICCAIS), 2012 International Conference on (pp. 344-348). IEEE.
26. Zhong, Y., & Deng, Y. (2014, September). Sensor orientation invariant mobile gait biometrics. In Biometrics (IJCB), 2014 IEEE International Joint Conference on (pp. 1-8). IEEE.
27. Thang, H. M., Viet, V. Q., Thuc, N. D., & Choi, D. (2012, November). Gait identification using accelerometer on mobile phone. In Control, Automation and Information Sciences (ICCAIS), 2012 International Conference on (pp. 344-348). IEEE.
28. Zhang, Y., Pan, G., Jia, K., Lu, M., Wang, Y., & Wu, Z. (2015). Accelerometer-based gait recognition by sparse representation of signature points with clusters. IEEE transactions on cybernetics, 45(9), 1864-1875.
29. Weiss, G. M., & Lockhart, J. W. (2011, August). Identifying user traits by mining smart phone accelerometer data. In Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data (pp. 61-69). ACM.
30. Jain, A., & Kanhangad, V. (2016, March). Investigating gender recognition in smartphones using accelerometer and gyroscope sensor readings. In Computational Techniques in Information and Communication Technologies (ICCTICT), 2016 International Conference on (pp. 597-602). IEEE.
31. Jain, A., & Kanhangad, V. (2018). Gender classification in smartphones using gait information. Expert Systems with Applications, 93, 257-266.
32. Seviş, K. N. (2017). Biometrics for smartphones: age recognition, gender recognition and idenfication (Doctoral dissertation).
33. Altun, K., Barshan, B., & Tunçel, O. (2010). Comparative study on classifying human activities with miniature inertial and magnetic sensors. Pattern Recognition, 43(10), 3605-3620.
34. Barshan, B., & Yüksek, M. C. (2014). Recognizing daily and sports activities in two open source machine learning environments using body-worn sensor units. The Computer Journal, 57(11), 1649-1667.
35. Altun, K., & Barshan, B. (2010, August). Human activity recognition using inertial/magnetic sensor units. In International Workshop on Human Behavior Understanding (pp. 38-51). Springer, Berlin, Heidelberg.
36. Kaya, Y., Uyar, M., Tekin, R., & Yıldırım, S. (2014). 1D-local binary pattern based feature extraction for classification of epileptic EEG signals. Applied Mathematics and Computation, 243, 209-219.
37. Zhao, Y., Jia, W., Hu, R. X., & Min, H. (2013). Completed robust local binary pattern for texture classification. Neurocomputing, 106, 68-76.
38. Avcı, E. (2007). Doku Tipi İmgelerin Sınıflandırılması İçin Bir Uyarlamalı Entropi Tabanlı Dalgacık-Yapay Sinir Ağı Sistemi. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 22(1).
39. Takçı, H., & Canbay, P. (2017). Kişisel verilerin korunmasında öznitelik tabanlı gizlilik etki değerlendirmesi yöntemi. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 32(4), 1301-1310.
40. Çelik, C., & Bilge, H. Ş. (2015). Ağırlıklandırılmış Koşullu Karşılıklı Bilgi İle Öznitelik Seçimi. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 30(4).

There are 40 citations in total.

Details

Primary Language	Turkish
Subjects	Engineering
Journal Section	Makaleler
Authors	Fatma Kuncan This is me 0000-0003-0712-6426 Yılmaz Kaya 0000-0001-5167-1101 Melih Kuncan 0000-0002-9749-0418
Publication Date	June 25, 2019
Submission Date	May 23, 2018
Acceptance Date	January 5, 2019
Published in Issue	Year 2019 Volume: 34 Issue: 4

Cite

APA	Kuncan, F., Kaya, Y., & Kuncan, M. (2019). Sensör işaretlerinden cinsiyet tanıma için yerel ikili örüntüler tabanlı yeni yaklaşımlar. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 34(4), 2173-2186. https://doi.org/10.17341/gazimmfd.426259
AMA	Kuncan F, Kaya Y, Kuncan M. Sensör işaretlerinden cinsiyet tanıma için yerel ikili örüntüler tabanlı yeni yaklaşımlar. GUMMFD. June 2019;34(4):2173-2186. doi:10.17341/gazimmfd.426259
Chicago	Kuncan, Fatma, Yılmaz Kaya, and Melih Kuncan. “Sensör işaretlerinden Cinsiyet tanıma için Yerel Ikili örüntüler Tabanlı Yeni yaklaşımlar”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 34, no. 4 (June 2019): 2173-86. https://doi.org/10.17341/gazimmfd.426259.
EndNote	Kuncan F, Kaya Y, Kuncan M (June 1, 2019) Sensör işaretlerinden cinsiyet tanıma için yerel ikili örüntüler tabanlı yeni yaklaşımlar. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 34 4 2173–2186.
IEEE	F. Kuncan, Y. Kaya, and M. Kuncan, “Sensör işaretlerinden cinsiyet tanıma için yerel ikili örüntüler tabanlı yeni yaklaşımlar”, GUMMFD, vol. 34, no. 4, pp. 2173–2186, 2019, doi: 10.17341/gazimmfd.426259.
ISNAD	Kuncan, Fatma et al. “Sensör işaretlerinden Cinsiyet tanıma için Yerel Ikili örüntüler Tabanlı Yeni yaklaşımlar”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 34/4 (June 2019), 2173-2186. https://doi.org/10.17341/gazimmfd.426259.
JAMA	Kuncan F, Kaya Y, Kuncan M. Sensör işaretlerinden cinsiyet tanıma için yerel ikili örüntüler tabanlı yeni yaklaşımlar. GUMMFD. 2019;34:2173–2186.
MLA	Kuncan, Fatma et al. “Sensör işaretlerinden Cinsiyet tanıma için Yerel Ikili örüntüler Tabanlı Yeni yaklaşımlar”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 34, no. 4, 2019, pp. 2173-86, doi:10.17341/gazimmfd.426259.
Vancouver	Kuncan F, Kaya Y, Kuncan M. Sensör işaretlerinden cinsiyet tanıma için yerel ikili örüntüler tabanlı yeni yaklaşımlar. GUMMFD. 2019;34(4):2173-86.