Ahmed Ben Jmaa

Publications (4) View all

• Source

  Available from: Ahmed Ben Jmaa

  Article: 2DPCA fractal features and genetic algorithm for efficient face representation and recognition

  Yousra Ben Jemaa, Ahmed Derbel, Ahmed Ben Jmaa
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  ABSTRACT: In this article, we present an automatic face recognition system. We show that fractal features obtained from Iterated Function System allow a successful face recognition and outperform the classical approaches. We propose a new fractal feature extraction algorithm based on genetic algorithms to speed up the feature extraction step. In order to capture the more important information that is contained in a face with a few fractal features, we use a bi-dimensional principal component analysis. We have shown with experimental results using two databases as to how the optimal recognition ratio and the recognition time make our system an effective tool for automatic face recognition. face recognition–fractal coding–2DPCA–IFS–genetic algorithms
  EURASIP Journal on Information Security 04/2012; 2011(1):1-9.
• Source

  Available from: Walid Mahdi

  Article: A new approach for digit recognition based on hand gesture analysis

  Ahmed Ben Jmaa, Walid Mahdi, Yousra Ben Jemaa, Abdelmajid Ben Hamadou
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  ABSTRACT: We present in this paper a new approach for hand gesture analysis that allows digit recognition. The analysis is based on extracting a set of features from a hand image and then combining them by using an induction graph. The most important features we extract from each image are the fingers locations, their heights and the distance between each pair of fingers. Our approach consists of three steps: (i) Hand detection and localization, (ii) fingers extraction and (iii) features identification and combination to digit recognition. Each input image is assumed to contain only one person, thus we apply a fuzzy classifier to identify the skin pixels. In the finger extraction step, we attempt to remove all the hand components except the fingers, this process is based on the hand anatomy properties. The final step consists on representing histogram of the detected fingers in order to extract features that will be used for digit recognition. The approach is invariant to scale, rotation and translation of the hand. Some experiments have been undertaken to show the effectiveness of the proposed approach. Comment: 8 Pages, International Journal of Computer Science and Information Security
  06/2009;
• Conference Proceeding: Hand Localization and Fingers Features Extraction: Application to Digit Recognition in Sign Language.

  Ahmed Ben Jmaa, Walid Mahdi, Yousra Ben Jemaa, Abdelmajid Ben Hamadou
  Intelligent Data Engineering and Automated Learning - IDEAL 2009, 10th International Conference, Burgos, Spain, September 23-26, 2009. Proceedings; 01/2009
• Chapter: Hand Localization and Fingers Features Extraction: Application to Digit Recognition in Sign Language

  A. Ben Jmaa, W. Mahdi, Y. Ben Jemaa, A. Ben Hamadou
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  ABSTRACT: We present in this paper an approach of hand gesture analysis that aims at recognizing a digit. The analysis is based on extracting a set of features from a hand image and then combining them by using an induction graph. The most important features we extract from each image are the fingers locations, their heights and the distance between each pair of fingers. Our approach consists of three steps: (i) Hand localization, (ii) fingers extraction and (iii) features identification and combination to digit recognition. Each input image is assumed to contain only one hand with black background, thus we apply a classifier based on one skin color to identify the skin pixels. In the finger extraction step, we attempt to remove all the hand components except the fingers, this process is based on the hand anatomy properties. The final step is based on histogram representation of the detected fingers which results in the features identification, which results in the digit recognition. The approach is invariant to scale, rotation and translation of the hand. Some experiments have been undertaken to show the effectivness of the proposed approach.
  09/2009: pages 151-159;