Rabab Ramadan

Publications (4) View all

• Conference Proceeding: Orientation of multiple principal axes shapes using efficient averaging method.

  Emad El-Sayed, Rehab F. Abdel-Kader, Rabab M. Ramadan
  Proceedings of the IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2010, December 15-18,2010, Luxor, Egypt; 01/2010
• Source

  Available from: Rabab Ramadan

  Article: Face Recognition Using Particle Swarm Optimization-Based Selected Features

  Rabab M. Ramadan, Rehab F. Abdel - Kader
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  ABSTRACT: Feature selection (FS) is a global optimization problem in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable recognition accuracy. It is the most important step that affects the performance of a pattern recognition system. This paper presents a novel feature selection algorithm based on particle swarm optimization (PSO). PSO is a computational paradigm based on the idea of collaborative behavior inspired by the social behavior of bird flocking or fish schooling. The algorithm is applied to coefficients extracted by two feature extraction techniques: the discrete cosine transforms (DCT) and the discrete wavelet transform (DWT). The proposedPSO-based feature selection algorithm is utilized to search the feature space for the optimal feature subset where features are carefully selected according to a well defined discrimination criterion. Evolution is driven by a fitness function defined in terms of maximizing the class separation (scatter index). The classifier performance and the length of selected feature vector are considered for performance evaluation using the ORL facedatabase. Experimental results show that the PSO-based feature selection algorithm was found to generate excellent recognition results with the minimal set of selected features.
  International Journal of Signal Processing, Image Processing and Pattern Recognition. 01/2009;
• Article: A Multimodal Biometric Fusion Approach based on Binary Particle Optimization

  W. Almayyan, H.S. Own, R. Ramadan H. Zedan
  Proceedings of AI-2011 Thirty-first SGAI International Conference on Artificial Intelligence, Cambridge, England,. 01/2011;
• Source

  Available from: Rawya Rizk

  Article: Rotation Invariant Face Recognition Based on Hybrid LPT/DCT Features

  Rehab F Abdel-Kader, Rabab M Ramadan, Rawya Y Rizk
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  ABSTRACT: The recognition of human faces, especially those with different orientations is a challenging and important problem in image analysis and classification. This paper proposes an effective scheme for rotation invariant face recognition using Log-Polar Transform and Discrete Cosine Transform combined features. The rotation invariant feature extraction for a given face image involves applying the log-polar transform to eliminate the rotation effect and to produce a row shifted log-polar image. The discrete cosine transform is then applied to eliminate the row shift effect and to generate the low-dimensional feature vector. A PSO-based feature selection algorithm is utilized to search the feature vector space for the optimal feature subset. Evolution is driven by a fitness function defined in terms of maximizing the between-class separation (scatter index). Experimental results, based on the ORL face database using testing data sets for images with different orientations; show that the proposed system outperforms other face recognition methods. The overall recognition rate for the rotated test images being 97%, demonstrating that the extracted feature vector is an effective rotation invariant feature set with minimal set of selected features.