In this paper, we propose a model for recognition of sign language being used by communication impaired people in the society. A novel method of extracting features from a video sequence of signs is proposed. Key frames are selected from a given video shots of signs to reduce the computational complexity yet retaining the significant information for recognition. A set of features is extracted from each key frame to capture the trajectory of hand movements made by the signer. The same sign made by different signers and by the same signers at different instances may have variations. The concept of symbolic data particularly interval type data is used to capture such variations and to efficiently represent signs in the knowledgebase. A suitable similarity measure is explored for the purpose of matching and recognition of signs. A database of signs made by communication impaired people of Mysore region is created and extensive experiments are conducted on this database to demonstrate the performance of the proposed approach.

Gait can be identified by observing static and dynamic parts of human body. In this paper a variant of gait energy image called change energy images (CEI) are generated to capture detailed static and dynamic information of human gait. Radon transform is applied to CEI in four different directions (vertical, horizontal and two opposite cross sections) considering four different angles to compute discriminative feature values. The extracted features are represented in the form of interval –valued type symbolic data. The proposed method is capable of recognizing an individual when he/she have variations in their gait due to different clothes they wear, in different normal conditions and carrying a bag. A similarity measure suitable for the proposed gait representation is explored for the purpose of establishing similarity match for gait recognition. Experiments are conducted on CASIA database B and the results have shown better recognition performance compared to some of the existing methods.