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In a previously reported work, the user's hand is represented as a weighted undirected complete connected graph and spectral properties of the graph are extracted and used as feature vectors. To reduce the complexity in representing the hand image as a complete connected graph and to achieve the higher identification rate, the hand image is sought to be represented as minimal edge connected graph. The experiments are conducted separately for 16 topologies of minimal edge connected graph selected empirically to investigate the performance of the hand-geometry system. The prominent edges of hand image graph are identified experimentally by computing the identification rate. In this study, an innovative peg-free handgeometry-based user identification system using spectral properties of a minimal edge connected graph representation of hand image is proposed. The multiclass support vector machine is employed for identification of the claimed user. The geometrical information embedded in the prominent edges will contribute to achieve better identification rate. The experimentation is carried on two databases, namely GPDS150 hand database and hand images of VTU-BEC-DB multimodal database. The minimal edge connected graph with 30 prominent edges of hand image graph achieves better identification with a faster rate.

Biometric authentication systems operating in real world environments using a single modality are found to be insecure and unreliable due to numerous limitations. Multimodal biometric systems have better accuracy and reliability due to the use of multiple biometric traits to authenticate a claimed identity or perform identification. In this paper a novel method for person identification using multimodal biometrics with hand geometry and palmprint biometric traits is proposed. The geometrical information embedded in the user hand and palmprint images are brought out through the graph representations. The topological characterization of the image moments, represented as the virtual nodes of the palmprint image graph is a novel feature of this work. The user hand and palmprint images are represented as weighted undirected graphs and spectral characteristics of the graphs are extracted as features vectors. The feature vectors of the hand geometry and palmprint are fused at feature level to obtain a graph spectral feature vector to represent the person. User identification is performed by using a multiclass support vector machine (SVM) classifier. The experimental results demonstrate, an appreciable performance giving identification rate of 99.19% for multimodal biometric after feature level fusion of hand geometry and palmprint modalities. The performance is investigated by conducting the experiments separately for handgeometry, palmprint and fused feature vectors for person identification. Experimental results show that the proposed multimodal system achieves better performance than the unimodal cues, and can be used in high security applications. Further comparison show that it is better than similar other multimodal techniques.