A proposed PCNN features quality optimization technique for pose-invariant 3D Arabic sign language recognition

This paper proposes a novel technique to deal with pose variations in 3D object recognition. This technique uses pulse-coupled neural network (PCNN) for image features generation from two different viewing angles. These signatures qualities are then evaluated, using a proposed fitness function. The features evaluation step is taken before any classification steps are performed. The evaluation results dynamic weighting factors for each camera express the features quality from the current viewing angles. The proposed technique uses the two 2D image features and their corresponding calculated weighting factors to construct optimized quality 3D features. An experiment was conducted in Arabic sign language recognition application which multiple views are necessary to distinguish some signs. The proposed technique obtained a 96% recognition accuracy for pose-invariant restrictions with a degree of freedom from 0 to 90.

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► This paper addresses features optimization to serve cameras weighting in 3D object recognition.
► 3D recognition is done using multiple views from different cameras.
► The system was implemented and employed in Arabic sign language recognition (ASLR) application.
► The model can be used to recognize any other sign languages.
► The system obtained recognition accuracy exceeds 90% on 3D hands models.