Intensity Based Distinctive Feature Extraction and Matching Using Scale Invariant Feature Transform for Indian Sign Language

India, having less awareness towards the deaf and dumb peoples leads to increase the communication gap between deaf and hard hearing community. Sign language is commonly developed for deaf and hard hearing peoples to convey their message by generating the different sign pattern. The scale invariant feature transform has been used to perform reliable matching between different images of the same object. This paper implements the various phases of scale invariant feature transform to extract the distinctive features from Indian sign language gestures. The intensity based feature extraction and matching has been performed using SIFT. The first part of experimental result shows the time constraint for each phase and the number of features extracted for 26 ISL gestures. The second part of experimental result shows the total number of key points matched when the intensity of the image varies from low to high. The matching percentage of approximately 98% is achieved.