Evaluation Method of Real-Time Face Detection
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Abstract
Object detection is one of computer technology using image or video. Face detection is closely related Image processing and computer vision are used to detect several objects including human faces, landscapes, cars, etc. Face detection algorithm aims to confirm if an image has a face as the object in it. In this study, face detection uses several methods, namely the Eigenface method, the Fisherface method, and the Local Binary Pattern Histogram (LBPH) method. This study used 10 different subjects. The test was carried out 15 times using each face detection method with constant distance. The face detection process in this study was simulated using JupyterLab. The result showed that LBPH method obtained the highest level of accuracy in between comparison among Fisherface method and the Eigenface method. The accuracy of the LBPH method is 93.90%, while the Eigenface method is 85% and the Fisherface method is 53.33%. Differences in face detection accuracy were found due to the low level of lighting in the room and the use of accessories on the subject.
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References
Mahmud, F., Khatun, M. T., Zuhori, S. T., Afroge, S., Aktar, M., & Pal, B. (2015, May). Face recognition using principle component analysis and linear discriminant analysis. In 2015 International Conference on Electrical Engineering and Information Communication Technology (ICEEICT) (pp. 1-4). IEEE.
Gaikawad, A. D., & Sonawane, P. D. (2016). An efficient video surveillance system using video based face recognition on real world data. IJSETR, 5(4).
Tu, Y. J., Kao, C. C., Lin, H. Y., & Chang, C. C. (2015). Face and gesture based human computer interaction. International Journal of Signal Processing, Image Processing and Pattern Recognition, 8(9), 219-228.
Wong, K. W., Lam, K. M., & Siu, W. C. (2001). An efficient algorithm for human face detection and facial feature extraction under different conditions. Pattern Recognition, 34(10), 1993-2004.
Viola, P., & Jones, M. (2001, December). Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001 (Vol. 1, pp. I-I). Ieee.
Kadir, K., Kamaruddin, M. K., Nasir, H., Safie, S. I., & Bakti, Z. A. K. (2014, August). A comparative study between LBP and Haar-like features for Face Detection using OpenCV. In 2014 4th International conference on engineering technology and technopreneuship (ICE2T) (pp. 335-339). IEEE.
Hewitt, R. (2007). Seeing with OpenCV: How Face Detection Works. webpage on Cognotics, Resources for Cognitive Robotics.
Lienhart, R., & Maydt, J. (2002, September). An extended set of haar-like features for rapid object detection. In Proceedings. international conference on image processing (Vol. 1, pp. I-I). IEEE.
Pissarenko, D. (2002). Eigenface-based facial recognition. December 1st, 584.
Jagtap, A. M., Kangale, V., Unune, K., & Gosavi, P. (2019, February). A Study of LBPH, Eigenface, Fisherface and Haar-like features for Face recognition using OpenCV. In 2019 International conference on intelligent sustainable systems (ICISS) (pp. 219-224). IEEE.
Dinalankara, L. (2017). Face detection & face recognition using open computer vision classifies. ResearchGate.
Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997). Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on pattern analysis and machine intelligence, 19(7), 711-720.
Hadid, A., Zhao, G., Ahonen, T., & Pietikäinen, M. (2008). Face analysis using local binary patterns. In Handbook of Texture Analysis (pp. 347-373).