Touch-less palm print recognition system based on fusion of local and global features

Rising demand for recognition methods that accurately work on low-resolution images acquired from a web-camera in a real-time environment with dynamic backgrounds inspires us to propose a hybrid feature extraction and fusion approach for palmprint recognition based on texture information available in the palm. On the topographic surface, the intrinsic surface curvature descriptor Hessian is used to characterise the unique texture profiles in the palmprint of an individual at global level. Local binary pattern-histogram features on the other hand being grey-scale and rotation invariant, capture local fine textures effectively. These local features are sensitive to position and orientation of the palm image. Canonical correlation analysis CCA is used to combine the features at the descriptor level which ensures that the information captured from both the features are maximally correlated and eliminate the redundant information giving a more compact representation. Experimental results on two databases used in this paper yield comparable results. Besides challenges like rotation, scale, projection, cluttered backgrounds and illumination, proposed method also handles burns, boils, cuts, dirt and oil stains on palms as challenges. To our knowledge, as an inception in literature, challenge of detecting closed-palms in real-time images is accomplished.