Probabilistic graph-based feature fusion and score fusion using SIFT features for face and ear biometrics
Dakshina Ranjan Kisku, Hunny Mehrotra, Phalguni Gupta, Jamuna Kanta Sing · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
Multibiometric systems offer more reliable and accurate performance combining the benefits of using multiple traits for user authentication. Due to incompatible biometric characteristics such as unmatched image patterns, improper feature registration and feature space representation, image scaling and unfeasible fusion schemes often degrades the performance of multibiometric systems. This paper focuses on the benefits of feature level and match score level fusions of face and ear biometrics using scale invariant feature transform (SIFT) representation and probabilistic graph. The proposed fusion techniques first compute and detect the SIFT features from face and ear images independently. Further probabilistic graphs are drawn on extracted feature points. By using iterative relaxation algorithm in both the graphs, which are drawn on face and ear images, corresponding feature points are searched and match points are paired and grouped into two independent sets. During feature level fusion, both the feature sets are concatenated together into an augmented group. Combined feature set is normalized using 'min-max' normalization rule and finally the concatenated feature vector is used for verification. In match score level fusion, independent verifications are performed using relaxation based probabilistic graphs and point pattern matching algorithm. As a result, independent matching scores generated from face and ear biometrics is fused together using 'sum' rule. The reported experimental results show the performance improvements in verification by applying feature level. and score level fusions.