Document Type : Research Paper
Department of Electrical and Electronics Engineering, TOBB University of Economics and Technology, Ankara, Turkey.
In recent years, development of the machine learning algorithms has led to the creation of intelligent surveillance systems. Thanks to the machine learning, it is possible to perform intelligent surveillance by recognizing people's facial features, classifying their age and gender, and detecting objects around instead of ordinary surveillance. In this study, a novel algorithm has been developed that classifies people's age and gender with a high accuracy rate. In addition, a novel object recognition algorithm has been developed that detects objects quickly and with high accuracy. In this study, age and gender classification was made based on the facial features of people using Convolutional Neural Network (CNN) architecture. Secondly, object detection was performed using different machine learning algorithms and the performance of the different machine learning algorithms was compared in terms of median average precision and inference time. The accuracy of the age and gender classification algorithm was tested using the Adience dataset and the results were graphed. The experimental results show that age and gender classification algorithms successfully classify people's age and gender. Then, the performances of object detection algorithms were tested using the COCO dataset and the results were presented in graphics. The experimental results stress that machine learning algorithms can successfully detect objects.
- Face detection
- Facial feature extraction
- Convolutional neural network
- Gender classification
- Age classification
- Machine learning
- Object detection
- Eyo, I. J., Adeoye, O. S., Inyang, U. G., & Umoeka, I. J. (2022). Hybrid intelligent parameter tuning approach for COVID-19 time series modeling and prediction. Journal of fuzzy extension and applications, 3(1), 64-80.
- Javanbakht, T., & Chakravorty, S. (2022). Prediction of human behavior with TOPSIS. Journal of fuzzy extension and applications, 3(2), 109-125.
- Benedict, S. R., & Kumar, J. S. (2016). Geometric shaped facial feature extraction for face recognition. 2016 IEEE international conference on advances in computer applications (ICACA)(pp. 275-278). IEEE.
- Wu, Y. M., Wang, H. W., Lu, Y. L., Yen, S., & Hsiao, Y. T. (2012). Facial feature extraction and applications: a review. Asian conference on intelligent information and database systems(pp. 228-238). Springer, Berlin, Heidelberg.
- Karahan, M., Kurt, H., & Kasnakoglu, C. (2020). Autonomous face detection and tracking using quadrotor UAV. 2020 4th international symposium on multidisciplinary studies and innovative technologies (ISMSIT)(pp. 1-4). IEEE.
- Lu, W. Y., & Ming, Y. A. N. G. (2019). Face detection based on viola-jones algorithm applying composite features. 2019 international conference on robots & intelligent system (ICRIS)(pp. 82-85). IEEE.
- Marčetić, D., Hrkać, T., & Ribarić, S. (2016). Two-stage cascade model for unconstrained face detection. 2016 first international workshop on sensing, processing and learning for intelligent machines (SPLINE)(pp. 1-4). IEEE.
- Gupta, S., & Singh, R. K. (2015). Mathematical morphology based face segmentation and facial feature extraction for facial expression recognition. 2015 international conference on futuristic trends on computational analysis and knowledge management (ABLAZE)(pp. 691-695). IEEE.
- Chowdhury, M., Gao, J., & Islam, R. (2016). Fuzzy rule based approach for face and facial feature extraction in biometric authentication. 2016 international conference on image and vision computing New Zealand (IVCNZ)(pp. 1-5). IEEE.
- Ko, J. B., Lee, W., Choi, S. E., & Kim, J. (2014). A gender classification method using age information. 2014 international conference on electronics, information and communications (ICEIC)(pp. 1-2). IEEE.
- Higashi, A., Yasui, T., Fukumizu, Y., & Yamauchi, H. (2011). Local Gabor directional pattern histogram sequence (LGDPHS) for age and gender classification. 2011 IEEE statistical signal processing workshop (SSP)(pp. 505-508). IEEE.
- Tang, C., Feng, Y., Yang, X., Zheng, C., & Zhou, Y. (2017). The object detection based on deep learning. 2017 4th international conference on information science and control engineering (ICISCE)(pp. 723-728). IEEE.
- Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: unified, real-time object detection. Proceedings of the IEEE conference on computer vision and pattern recognition(pp. 779-788). IEEE.
- Li, Y., Dua, A., & Ren, F. (2020). Light-weight retinanet for object detection on edge devices. 2020 IEEE 6th world forum on internet of things (WF-IoT)(pp. 1-6). IEEE.
- Karahan, M., Lacinkaya, F., Erdonmez, K., Eminagaoglu, E. D., & Kasnakoglu, C. (2021). Face detection and facial feature extraction with machine learning. International conference on intelligent and fuzzy systems(pp. 205-213). Springer, Cham.
- Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition, CVPR 2001(pp. I-I). IEEE.
- Ayvaz, U., & Gürüler, H. (2017). The detection of emotional expression towards computer users. International journal of informatics technologies, 10(2), 231-239.
- Lin, S. D., & Chen, K. (2019). Illumination invariant thermal face recognition using convolutional neural network. 2019 IEEE international conference on consumer electronics-Asia (ICCE-Asia)(pp. 83-84). IEEE.
- Eidinger, E., Enbar, R., & Hassner, T. (2014). Age and gender estimation of unfiltered faces. IEEE Transactions on information forensics and security, 9(12), 2170-2179.
- Jung, H., Kim, B., Lee, I., Yoo, M., Lee, J., Ham, S., ... & Kang, J. (2018). Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network. PloS one, 13(9), e0203355. https://doi.org/10.1371/journal.pone.0203355
- Punn, N. S., Sonbhadra, S. K., Agarwal, S., & Rai, G. (2020). Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and Deepsort techniques. Retrieved from https://doi.org/10.48550/arXiv.2005.01385
- Redmon, J., & Farhadi, A. (2018). Yolov3: an incremental improvement. Retrieved from https://doi.org/10.48550/arXiv.1804.02767
- Viola, P., Jones, M. J., & Snow, D. (2005). Detecting pedestrians using patterns of motion and appearance. International journal of computer vision, 63(2), 153-161.