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Gender Classification with Low-Power Laser Signals

Yıl 2021, Cilt: 4 Sayı: 2, 62 - 71, 19.08.2021

Nevzat Olgun İbrahim Türkoğlu

Öz

Gender classification can provide significant advantages in applications with access control, marketing activities and biometric verification processes. In cases where the entries to some areas are only male or female, advertising products according to the number of male and female in the store or reducing the database usage burden by primarily gender discrimination in biometric verification can be given as examples of gender classification practices. Gender classification is a binary classification problem as male or female. In traditional methods, gender classification has been made from facial images. One of the biggest difficulties in gender classification from facial images is that the person's face cannot be kept in a certain position, while other is the difficulties in the imaging stage. The desire of the person to hide herself from the cameras, differences in the face and lighting conditions can be given as examples of the difficulties of the image-based methods. In this study, we propose gender classification with low-power laser beams instead of traditional camera-based method of gender classification. In the experimental study conducted for this purpose, a low-powered laser beam is projected onto the subjects 'arm for a short period of time from a distance of 2 m, and laser signals reflected from the subjects' arm are recorded. Laser signals reflected from the arm of subjects are classified according to the LSTM deep learning architecture after data preparation, and the subjects' gender is determined. An average classification success rate of 76.20% was achieved as a result of the gender classification study in which 6 men and 6 women between the ages of 19 and 38 participated. The results show that gender classification can be performed with laser signals. Another advantage of this method is that the arm can be easily positioned at the desired location during the receiving signal from the person's arm.

Anahtar Kelimeler

Gender Classification Laser LSTM Sex Classification Signal Processing

Kaynakça

  • Nayak JS, Indiramma M. An approach to enhance age invariant face recognition performance based on gender classification. Journal of King Saud University - Computer and Information Sciences 2021. https://doi.org/10.1016/j.jksuci.2021.01.005.
  • Juefei-Xu F, Verma E, Savvides M. Deepgender2: A generative approach toward occlusion and low-resolution robust facial gender classification via progressively trained attention shift convolutional neural networks (PTAS-CNN) and deep convolutional generative adversarial networks (DCGAN). Advances in Computer Vision and Pattern Recognition, vol. PartF1, Springer London; 2017, p. 183–218. https://doi.org/10.1007/978-3-319-61657-5_8.
  • Ali S, Wu Z, Zhou M, Du G, Li X, Pengcheng F. Human identification using sensors data based on 3D gait area. Proceedings - 2014 International Conference on Cyberworlds, CW 2014, Institute of Electrical and Electronics Engineers Inc.; 2014, p. 285–92. https://doi.org/10.1109/CW.2014.46.
  • Anchal S, Mukhopadhyay B, Kar S. Predicting gender from footfalls using a seismic sensor. 2017 9th International Conference on Communication Systems and Networks, COMSNETS 2017, Institute of Electrical and Electronics Engineers Inc.; 2017, p. 47–54. https://doi.org/10.1109/COMSNETS.2017.7945357.
  • Mustafa A, Meehan K. Gender Classification and Age Prediction using CNN and ResNet in Real-Time. 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020, Institute of Electrical and Electronics Engineers Inc.; 2020, p. 1–6. https://doi.org/10.1109/ICDABI51230.2020.9325696.
  • Do TD, Nguyen VH, Kim H. Real-time and robust multiple-view gender classification using gait features in video surveillance. Pattern Analysis and Applications 2020;23:399–413. https://doi.org/10.1007/s10044-019-00802-6.
  • Sengupta S, Yasmin G, Ghosal A. Classification of male and female speech using perceptual features. 8th International Conference on Computing, Communications and Networking Technologies, ICCCNT 2017, Institute of Electrical and Electronics Engineers Inc.; 2017, p. 1–7. https://doi.org/10.1109/ICCCNT.2017.8204065.
  • Qadri SAA, Gunawan TS, Wani T, Alghifari MF, Mansor H, Kartiwi M. Comparative Analysis of Gender Identification using Speech Analysis and Higher Order Statistics. 2019 IEEE 6th International Conference on Smart Instrumentation, Measurement and Application, ICSIMA 2019, Institute of Electrical and Electronics Engineers Inc.; 2019, p. 1–6. https://doi.org/10.1109/ICSIMA47653.2019.9057296.
  • Bales D, Tarazaga PA, Kasarda M, Batra D, Woolard AG, Poston JD, et al. Gender Classification of Walkers via Underfloor Accelerometer Measurements. IEEE Internet of Things Journal 2016;3:1259–66. https://doi.org/10.1109/JIOT.2016.2582723.
  • Nixon MS, Carter JN. Automatic recognition by gait. Proceedings of the IEEE 2006;94:2013–24. https://doi.org/10.1109/JPROC.2006.886018.
  • Kwon B, Lee S. Joint Swing Energy for Skeleton-Based Gender Classification. IEEE Access 2021;9:28334–48. https://doi.org/10.1109/ACCESS.2021.3058745.
  • Zhang D, Wang Y. Using multiple views for gait-based gender classification. 26th Chinese Control and Decision Conference, CCDC 2014, IEEE Computer Society; 2014, p. 2194–7. https://doi.org/10.1109/CCDC.2014.6852532.
  • Russel NS, Selvaraj A. Gender discrimination, age group classification and carried object recognition from gait energy image using fusion of parallel convolutional neural network. IET Image Processing 2021;15:239–51. https://doi.org/10.1049/ipr2.12024.
  • Dileep MR, Danti A. Human Age and Gender Prediction Based on Neural Networks and Three Sigma Control Limits. Applied Artificial Intelligence 2018;32:281–92. https://doi.org/10.1080/08839514.2018.1451217.
  • Pathak AR, Pandey M, Rautaray S. Application of Deep Learning for Object Detection. Procedia Computer Science, vol. 132, 2018, p. 1706–17. https://doi.org/10.1016/j.procs.2018.05.144.
  • Doğan F, Türkoğlu İ. Derin öğrenme algoritmalarının yaprak sınıflandırma başarımlarının karşılaştırılması. Sakarya University Journal of Computer and Information Sciences 2018;1:10–21.
  • Betul A, Gurses OA, Oktay AB, Gurses A. Automatic Detection, Localization and Segmentation of Nano-Particles with Deep Learning in Microscopy Images. Micron 2019;120:113–9. https://doi.org/10.1016/j.micron.2019.02.009.
  • Wang D, Chen J. Supervised speech separation based on deep learning: An overview. IEEE/ACM Transactions on Audio Speech and Language Processing 2018. https://doi.org/10.1109/TASLP.2018.2842159.
  • Peris Á, Casacuberta F. Online learning for effort reduction in interactive neural machine translation. Computer Speech and Language 2019. https://doi.org/10.1016/j.csl.2019.04.001.
  • Chatterjee A, Gupta U, Chinnakotla MK, Srikanth R, Galley M, Agrawal P. Understanding Emotions in Text Using Deep Learning and Big Data. Computers in Human Behavior 2019. https://doi.org/10.1016/j.chb.2018.12.029.
  • Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T. The rise of deep learning in drug discovery. Drug Discovery Today 2018. https://doi.org/10.1016/j.drudis.2018.01.039.
  • Greff K, Srivastava RK, Koutnik J, Steunebrink BR, Schmidhuber J. LSTM: A Search Space Odyssey. IEEE Transactions on Neural Networks and Learning Systems 2017;28:2222–32. https://doi.org/10.1109/TNNLS.2016.2582924.
  • Olgun N, Turkoğlu İ. Classification of Live/Lifeless Assets with Laser Beams in Different Humidity Environments. 8th International Symposium on Digital Forensics and Security, ISDFS 2020, 2020. https://doi.org/10.1109/ISDFS49300.2020.9116314.
  • Olgun N, Türkoğlu İ. Lazer İşaretleri ile Otomatik Hedef Tanıma. Sak Univ J Comput Inf Sci 2018;1:1–10.
  • Olgun N, Türkoğlu İ. Defining Objects with Laser from a Long Distance via Deep Learning Networks. 10th International Symposium on Intelligent Manufacturing and Service Systems (IMSS’19), Sakarya,Turkey: 2019, p. 1401–11.
  • Vaseghi S v. Advanced Digital Signal Processing and Noise Reduction. Chichester, UK: John Wiley & Sons, Ltd; 2001. https://doi.org/10.1002/0470841621.
  • Buttkus B. Estimation of the Power Spectral Density Function. Spectral Analysis and Filter Theory in Applied Geophysics, Springer Berlin Heidelberg; 2000, p. 179–213. https://doi.org/10.1007/978-3-642-57016-2_10.
  • Kang C, Zhang X, Zhang A, Lin H. Underwater acoustic targets classification using welch spectrum estimation and neural networks. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2004;3173:930–5. https://doi.org/10.1007/978-3-540-28647-9_153.
  • Balasaraswathy N, Rajavel R. Low-complexity power spectral density estimation. Advances in Intelligent Systems and Computing, vol. 325, Springer Verlag; 2015, p. 273–82. https://doi.org/10.1007/978-81-322-2135-7_30.
  • Alkan A, Kiymik MK. Comparison of AR and Welch methods in epileptic seizure detection. Journal of Medical Systems 2006;30:413–9. https://doi.org/10.1007/s10916-005-9001-0.
  • Yildirim Ö. A novel wavelet sequences based on deep bidirectional LSTM network model for ECG signal classification. Computers in Biology and Medicine 2018;96:189–202. https://doi.org/10.1016/j.compbiomed.2018.03.016.
  • Michielli N, Acharya UR, Molinari F. Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals. Computers in Biology and Medicine 2019;106:71–81. https://doi.org/10.1016/j.compbiomed.2019.01.013.
  • Garg A, Kapoor A, Bedi AK, Sunkaria RK. Merged LSTM Model for emotion classification using EEG signals. 2019 International Conference on Data Science and Engineering, ICDSE 2019, Institute of Electrical and Electronics Engineers Inc.; 2019, p. 139–43. https://doi.org/10.1109/ICDSE47409.2019.8971484.

Yıl 2021, Cilt: 4 Sayı: 2, 62 - 71, 19.08.2021

Nevzat Olgun İbrahim Türkoğlu

Öz

Kaynakça

  • Nayak JS, Indiramma M. An approach to enhance age invariant face recognition performance based on gender classification. Journal of King Saud University - Computer and Information Sciences 2021. https://doi.org/10.1016/j.jksuci.2021.01.005.
  • Juefei-Xu F, Verma E, Savvides M. Deepgender2: A generative approach toward occlusion and low-resolution robust facial gender classification via progressively trained attention shift convolutional neural networks (PTAS-CNN) and deep convolutional generative adversarial networks (DCGAN). Advances in Computer Vision and Pattern Recognition, vol. PartF1, Springer London; 2017, p. 183–218. https://doi.org/10.1007/978-3-319-61657-5_8.
  • Ali S, Wu Z, Zhou M, Du G, Li X, Pengcheng F. Human identification using sensors data based on 3D gait area. Proceedings - 2014 International Conference on Cyberworlds, CW 2014, Institute of Electrical and Electronics Engineers Inc.; 2014, p. 285–92. https://doi.org/10.1109/CW.2014.46.
  • Anchal S, Mukhopadhyay B, Kar S. Predicting gender from footfalls using a seismic sensor. 2017 9th International Conference on Communication Systems and Networks, COMSNETS 2017, Institute of Electrical and Electronics Engineers Inc.; 2017, p. 47–54. https://doi.org/10.1109/COMSNETS.2017.7945357.
  • Mustafa A, Meehan K. Gender Classification and Age Prediction using CNN and ResNet in Real-Time. 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020, Institute of Electrical and Electronics Engineers Inc.; 2020, p. 1–6. https://doi.org/10.1109/ICDABI51230.2020.9325696.
  • Do TD, Nguyen VH, Kim H. Real-time and robust multiple-view gender classification using gait features in video surveillance. Pattern Analysis and Applications 2020;23:399–413. https://doi.org/10.1007/s10044-019-00802-6.
  • Sengupta S, Yasmin G, Ghosal A. Classification of male and female speech using perceptual features. 8th International Conference on Computing, Communications and Networking Technologies, ICCCNT 2017, Institute of Electrical and Electronics Engineers Inc.; 2017, p. 1–7. https://doi.org/10.1109/ICCCNT.2017.8204065.
  • Qadri SAA, Gunawan TS, Wani T, Alghifari MF, Mansor H, Kartiwi M. Comparative Analysis of Gender Identification using Speech Analysis and Higher Order Statistics. 2019 IEEE 6th International Conference on Smart Instrumentation, Measurement and Application, ICSIMA 2019, Institute of Electrical and Electronics Engineers Inc.; 2019, p. 1–6. https://doi.org/10.1109/ICSIMA47653.2019.9057296.
  • Bales D, Tarazaga PA, Kasarda M, Batra D, Woolard AG, Poston JD, et al. Gender Classification of Walkers via Underfloor Accelerometer Measurements. IEEE Internet of Things Journal 2016;3:1259–66. https://doi.org/10.1109/JIOT.2016.2582723.
  • Nixon MS, Carter JN. Automatic recognition by gait. Proceedings of the IEEE 2006;94:2013–24. https://doi.org/10.1109/JPROC.2006.886018.
  • Kwon B, Lee S. Joint Swing Energy for Skeleton-Based Gender Classification. IEEE Access 2021;9:28334–48. https://doi.org/10.1109/ACCESS.2021.3058745.
  • Zhang D, Wang Y. Using multiple views for gait-based gender classification. 26th Chinese Control and Decision Conference, CCDC 2014, IEEE Computer Society; 2014, p. 2194–7. https://doi.org/10.1109/CCDC.2014.6852532.
  • Russel NS, Selvaraj A. Gender discrimination, age group classification and carried object recognition from gait energy image using fusion of parallel convolutional neural network. IET Image Processing 2021;15:239–51. https://doi.org/10.1049/ipr2.12024.
  • Dileep MR, Danti A. Human Age and Gender Prediction Based on Neural Networks and Three Sigma Control Limits. Applied Artificial Intelligence 2018;32:281–92. https://doi.org/10.1080/08839514.2018.1451217.
  • Pathak AR, Pandey M, Rautaray S. Application of Deep Learning for Object Detection. Procedia Computer Science, vol. 132, 2018, p. 1706–17. https://doi.org/10.1016/j.procs.2018.05.144.
  • Doğan F, Türkoğlu İ. Derin öğrenme algoritmalarının yaprak sınıflandırma başarımlarının karşılaştırılması. Sakarya University Journal of Computer and Information Sciences 2018;1:10–21.
  • Betul A, Gurses OA, Oktay AB, Gurses A. Automatic Detection, Localization and Segmentation of Nano-Particles with Deep Learning in Microscopy Images. Micron 2019;120:113–9. https://doi.org/10.1016/j.micron.2019.02.009.
  • Wang D, Chen J. Supervised speech separation based on deep learning: An overview. IEEE/ACM Transactions on Audio Speech and Language Processing 2018. https://doi.org/10.1109/TASLP.2018.2842159.
  • Peris Á, Casacuberta F. Online learning for effort reduction in interactive neural machine translation. Computer Speech and Language 2019. https://doi.org/10.1016/j.csl.2019.04.001.
  • Chatterjee A, Gupta U, Chinnakotla MK, Srikanth R, Galley M, Agrawal P. Understanding Emotions in Text Using Deep Learning and Big Data. Computers in Human Behavior 2019. https://doi.org/10.1016/j.chb.2018.12.029.
  • Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T. The rise of deep learning in drug discovery. Drug Discovery Today 2018. https://doi.org/10.1016/j.drudis.2018.01.039.
  • Greff K, Srivastava RK, Koutnik J, Steunebrink BR, Schmidhuber J. LSTM: A Search Space Odyssey. IEEE Transactions on Neural Networks and Learning Systems 2017;28:2222–32. https://doi.org/10.1109/TNNLS.2016.2582924.
  • Olgun N, Turkoğlu İ. Classification of Live/Lifeless Assets with Laser Beams in Different Humidity Environments. 8th International Symposium on Digital Forensics and Security, ISDFS 2020, 2020. https://doi.org/10.1109/ISDFS49300.2020.9116314.
  • Olgun N, Türkoğlu İ. Lazer İşaretleri ile Otomatik Hedef Tanıma. Sak Univ J Comput Inf Sci 2018;1:1–10.
  • Olgun N, Türkoğlu İ. Defining Objects with Laser from a Long Distance via Deep Learning Networks. 10th International Symposium on Intelligent Manufacturing and Service Systems (IMSS’19), Sakarya,Turkey: 2019, p. 1401–11.
  • Vaseghi S v. Advanced Digital Signal Processing and Noise Reduction. Chichester, UK: John Wiley & Sons, Ltd; 2001. https://doi.org/10.1002/0470841621.
  • Buttkus B. Estimation of the Power Spectral Density Function. Spectral Analysis and Filter Theory in Applied Geophysics, Springer Berlin Heidelberg; 2000, p. 179–213. https://doi.org/10.1007/978-3-642-57016-2_10.
  • Kang C, Zhang X, Zhang A, Lin H. Underwater acoustic targets classification using welch spectrum estimation and neural networks. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2004;3173:930–5. https://doi.org/10.1007/978-3-540-28647-9_153.
  • Balasaraswathy N, Rajavel R. Low-complexity power spectral density estimation. Advances in Intelligent Systems and Computing, vol. 325, Springer Verlag; 2015, p. 273–82. https://doi.org/10.1007/978-81-322-2135-7_30.
  • Alkan A, Kiymik MK. Comparison of AR and Welch methods in epileptic seizure detection. Journal of Medical Systems 2006;30:413–9. https://doi.org/10.1007/s10916-005-9001-0.
  • Yildirim Ö. A novel wavelet sequences based on deep bidirectional LSTM network model for ECG signal classification. Computers in Biology and Medicine 2018;96:189–202. https://doi.org/10.1016/j.compbiomed.2018.03.016.
  • Michielli N, Acharya UR, Molinari F. Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals. Computers in Biology and Medicine 2019;106:71–81. https://doi.org/10.1016/j.compbiomed.2019.01.013.
  • Garg A, Kapoor A, Bedi AK, Sunkaria RK. Merged LSTM Model for emotion classification using EEG signals. 2019 International Conference on Data Science and Engineering, ICDSE 2019, Institute of Electrical and Electronics Engineers Inc.; 2019, p. 139–43. https://doi.org/10.1109/ICDSE47409.2019.8971484.
Toplam 33 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Nevzat Olgun

İbrahim Türkoğlu 0000-0003-4938-4167

Yayımlanma Tarihi 19 Ağustos 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 4 Sayı: 2

Kaynak Göster

APA Olgun, N., & Türkoğlu, İ. (2021). Gender Classification with Low-Power Laser Signals. Veri Bilimi, 4(2), 62-71.


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