TR
Gender Classification with Low-Power Laser Signals
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
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.
Ayrıntılar
Birincil Dil
İngilizce
Konular
Mühendislik
Bölüm
Araştırma Makalesi
Yayımlanma Tarihi
19 Ağustos 2021
Gönderilme Tarihi
5 Temmuz 2021
Kabul Tarihi
29 Temmuz 2021
Yayımlandığı Sayı
Yıl 2021 Cilt: 4 Sayı: 2
APA
Olgun, N., & Türkoğlu, İ. (2021). Gender Classification with Low-Power Laser Signals. Veri Bilimi, 4(2), 62-71. https://izlik.org/JA64FK92DB
AMA
1.Olgun N, Türkoğlu İ. Gender Classification with Low-Power Laser Signals. Veri Bilim Derg. 2021;4(2):62-71. https://izlik.org/JA64FK92DB
Chicago
Olgun, Nevzat, ve İbrahim Türkoğlu. 2021. “Gender Classification with Low-Power Laser Signals”. Veri Bilimi 4 (2): 62-71. https://izlik.org/JA64FK92DB.
EndNote
Olgun N, Türkoğlu İ (01 Ağustos 2021) Gender Classification with Low-Power Laser Signals. Veri Bilimi 4 2 62–71.
IEEE
[1]N. Olgun ve İ. Türkoğlu, “Gender Classification with Low-Power Laser Signals”, Veri Bilim Derg, c. 4, sy 2, ss. 62–71, Ağu. 2021, [çevrimiçi]. Erişim adresi: https://izlik.org/JA64FK92DB
ISNAD
Olgun, Nevzat - Türkoğlu, İbrahim. “Gender Classification with Low-Power Laser Signals”. Veri Bilimi 4/2 (01 Ağustos 2021): 62-71. https://izlik.org/JA64FK92DB.
JAMA
1.Olgun N, Türkoğlu İ. Gender Classification with Low-Power Laser Signals. Veri Bilim Derg. 2021;4:62–71.
MLA
Olgun, Nevzat, ve İbrahim Türkoğlu. “Gender Classification with Low-Power Laser Signals”. Veri Bilimi, c. 4, sy 2, Ağustos 2021, ss. 62-71, https://izlik.org/JA64FK92DB.
Vancouver
1.Nevzat Olgun, İbrahim Türkoğlu. Gender Classification with Low-Power Laser Signals. Veri Bilim Derg [Internet]. 01 Ağustos 2021;4(2):62-71. Erişim adresi: https://izlik.org/JA64FK92DB