Fusion Based Hand Geometry Recognition Using Dempster-Shafer Theory
This work addresses biometric identification and verification using hand geometry, offering a robust method for security applications, though it is incremental in its approach.
The paper tackles person recognition by fusing hand geometry features from both hands without pose restrictions, achieving a correct identification rate of 99.5% and a False Acceptance Rate of 0.625% in verification experiments with 201 subjects.
This paper presents a new technique for person recognition based on the fusion of hand geometric features of both the hands without any pose restrictions. All the features are extracted from normalized left and right hand images. Fusion is applied at feature level and also at decision level. Two probability based algorithms are proposed for classification. The first algorithm computes the maximum probability for nearest three neighbors. The second algorithm determines the maximum probability of the number of matched features with respect to a thresholding on distances. Based on these two highest probabilities initial decisions are made. The final decision is considered according to the highest probability as calculated by the Dempster-Shafer theory of evidence. Depending on the various combinations of the initial decisions, three schemes are experimented with 201 subjects for identification and verification. The correct identification rate found to be 99.5%, and the False Acceptance Rate (FAR) of 0.625% has been found during verification.