Main Article Content
The purpose of this study was to apply face recognition of the criminal face detection, which this system implemented Eigen face to analyze face details in facial comparison database. The data used in system assessment of efficiency and developed software. The set of images used in this analysis comprised 30 images divided into 3 groups, each group contained 10 images. The results were as follow; data set 1 consisted of 10 images using image resolution of 100x100 pixel, 9 images were accurately accurate with 90% precision; data set 2 consisted of 10 images using image resolution of 150x150 pixel, 8 images were accurate with 80% precision; data set 3 consisted of 10 images using image resolution of 200x200 pixel, 7 images were accurate with 70% precision. The mean of system was 80% considered as good efficiency. Overall, the developed system had good efficiency. The system considered as quite precise and suitable for application in criminal face detection and can be used for further development.
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• Atalay, I., & Gökmen, M. (1996). Face recognition using Eigenfaces. SIU1996, Antalya, Turkey, 151-156.
• Çarıkçı, M., & Özen, F. (2012). A face recognition system based on Eigenfaces method. Procedia Technology, 1(2012), 118-123.
• Davis M., Popov, S., & Surlea, C. (2010). Real-time face recognition from surveillance video. Intelligent Video Event Analysis and Understanding, 332, 155–194.
• Hamid, R.A., & Thom, J.A. (2010). Criteria that have an effect on users while making image relevance judgements. In the fifteenth Australasian Document Computing Symposium, 76-83.
• Henry, A. (2012). Five best webcams. Lifehacker. Retrieved 29 March 2021, Retrieved from https://lifehacker.com/five-best-webcams-5961369
• Karaduman, B. (2008). Relevant component analysis. [Thesis, YıldızTechnical University, Turkey].
• Kotropoulos, C., & Pitas, I. (1997). Rule-based face detection in frontal views. In International conference on Acoustics, Speech, and Signal Processing, 2537-2540.
• Lin, S.H. (2000). An introduction to face recognition technology. Informing Science Special Issues on Multimedia Informing Technologies, 3(1).
• Marciniak, T., Drgas, S., & Cetnarowicz, D. (2011). Fast face location using AdaBoost algorithm and identification with matrix decomposition methods. In the International Conference on Multimedia Communications, Services and Security, 242-250.
• Rathi, R., Choudhary, M. and Chandra, B. (2012). An application of face recognition system using image processing and neural networks. International Journal Computer Technology Application, 3(1), 45-49.
• The Database of Faces (AT&T), Retrieved 20 March 2021, Retrieved from https://git-disl.github.io/GTDLBench/datasets/att_face_dataset/
• Tolba, A.S., El-Baz, A.H., & El-Harby, A.A. (2005). Face recognition: A literature review. International Journal of Signal Processing, 2(2), 88-103.
• Turk, M., & Pentland, A. (1991). Eigen faces for recognition. Journal of Cognitive Neuroscience, 3(1), 71-86.
• Yang, G.Z., & Huang, T.S. (1994). Human face detection in a complex background. Pattern Recognition, 27(1), 53-63.
• Yang, M.H., Kriegman, D.J. and Ahuja, N. (2002). Detecting faces in images: A survey. IEEE Transaction on Pattern Analysis & Machine Intelligence, 24(1), 34-58.