Word Sign Recognition of Invariant Images Based on SURF with Laplacian Eigenmaps

This paper presents a recognition of sign language in the area of computer vision and pattern recognition system. The local features of invariant images were extracted using speeded up robust features (SURF) with dimensionality reduction techniques. Then, K-nearest neighbour classification technique is used for establishing the recognition system. The local feature descriptor of SURF was computationally complex for classifying the word signs. Laplacian eigenmaps has been combined with SURF to reduce dimensionality of feature descriptor and computation time for classification. In this paper, execution of recognition rate has been developed by using Laplacian eigenmaps of dimensionality reduction compared with other methods of principal component analysis and singular value decomposition. By applying Laplacian eigenmaps, sign classification accuracy was improved from 90 to 96% than the dimensionality reduction strategy.