Sanjeevakumar M. Hatture

Work place: Department of Computer Science and Engineering, Basaveshwar Engineering College, Bagalkot - 587103, Karnataka State, India



Research Interests: Pattern Recognition, Network Architecture, Network Security, Image Processing, Data Structures and Algorithms


Sanjeevakumar. M. Hatture received the Bachelor’s Degree in Electronics and Communication Engineering from Karnataka University, Dharwad, Karnataka, India, and the Master Degree in Computer Science and Engineering from Visvesvaraya Technological University, Belagavi, Karnataka, India, and currently pursuing the Ph.D Degree in the Department of Computer Science and Engineering at Basaveshwar Engineering College, Bagalkot under Visvesvaraya Technological University, Belagavi, Karnataka, India. His research interests include biometrics, image processing, pattern recognition, soft-computing and network security. He is life member of professional bodies like IEI and ISTE.

Author Articles
Biometric Person Identification System: A Multimodal Approach Employing Spectral Graph Characteristics of Hand Geometry and Palmprint

By Shanmukhappa A. Angadi Sanjeevakumar M. Hatture

DOI:, Pub. Date: 8 Mar. 2016

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.

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