Online Face Detection and Recognition in a Video Stream | ||
| AL-Rafidain Journal of Computer Sciences and Mathematics | ||
| Article 18, Volume 10, Issue 4, December 2013, Pages 115-134 PDF (658.13 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.33899/csmj.2013.163560 | ||
| Authors | ||
| Maha A. Hasso Al-Ghurery; Manar A. Zidan Al-Abaji | ||
| College of Computer Science and Mathematics University of Mosul, Mosul, Iraq | ||
| Abstract | ||
| Faces detection and recognition process received considerable attention during the past decade, it is still considered one of the most important studies in the field of Image Processing, Pattern Recognition, and Computer Vision, where it drew the attention of many researchers from both the academic and industrial environments because of the broad scope of its practical applications. Face recognition system is considered as one of the biometric information processing systems that is easy to apply, the necessity of identifying the identity in the areas of security and surveillance systems made face recognition system one of the most important biometric techniques used in the identification of the individual. In this work a system is designed for Online Face Detection and Recognition depending on multiple algorithms that are: AdaBoost algorithm for the face detection and the two algorithms Principle Component Analysis (PCA) and Linear Discriminate Analysis (LDA) to extract features and use back propagation neural network in recognition. This system has been applied on a group of people using different numbers of features extracted from the face. Good recognition ratios have been obtained reached (87.5%) relying on the key frame in the calculation of the of recognition ratio and (94%) relying on the outcome of the 25 video frames in the calculation of the of recognition ratio. The System was implemented using graphical interfaces of Visual Studio C# 2010 Language. | ||
| Keywords | ||
| Face detection; AdaBoost algorithm; Principle Component Analysis (PCA); Linear Discriminate Analysis (LDA); back propagation neural network | ||
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