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In Sign language Recognition (SLR) system, signs are identified on the basis of hand shapes. Zernike Moments (ZM) are used as an effective shape descriptor in the field of Pattern Recognition. These are derived from orthogonal Zernike polynomial. The Zernike polynomial characteristics change as order and iteration parameter are varied. Observing their behaviour gives an insight into the selection of a particular value of ZM as a part of an optimal feature vector. The performance of ZMs can be improved by combining it with other features, therefore, ZMs are combined with Hu Moments (HM) and Geometric features (GF). An optimal feature vector of size 56 is proposed for ISL dataset. The importance of the internal edge details to address issue of hand-over-hand occlusion is also highlighted in the paper. The proposed feature set gives high accuracy for Support Vector Machine (SVM), Logistic Model Tree (LMT) and Multilayer Perceptron (MLP). However, the accuracy of Bayes Net (BN), Nave Bayes (NB), J48 and kNearest Neighbour (k-NN) improves significantly for Info Gain based normalized feature set.

CFS- InfoGain based Combined Shape-based Feature Vector for Signer Independent ISL Database