Identity verification using shape and geometry of human hands

• Shape and geometry features are encoded from contour of the hand only.
• Robust preprocessing is introduced to cope with the noise and disjoint fingers.
• Hand orientation and finger registration is applied to provide more flexibility.
• Two level score fusion is adopted to enhance the verification performance.
• Promising results are obtained over contact and contactless (IITD) datasets.

A multimodal biometric system for personal identity verification is proposed using hand shape and hand geometry in this paper. Shape and geometry features are derived with the help of only contour of the hand image for which only one image acquisition device is sufficient. All the processing is done with respect to a stable reference point at wrist line which is more stable as compared to the centroid against the finger rotation and peaks and valleys determination. Two shape based features are extracted by using the distance and orientation of each point of hand contour with respect to the reference point followed by wavelet decomposition to reduce the dimension. Seven distances are used to encode the geometrical information of the hand. Shape and geometry based features are fused at score levels and their performances are evaluated using standard ROC curves between false acceptance rate, true acceptance rate, equal error rate and decidability index. Different similarity measures are used to examine the accuracy of the introduced method. Performance of system is analyzed for shape based (distance and orientation) and geometrical features individually as well as for all possible combinations of feature and score level fusion. The proposed features and fusion methods are studied over two hand image datasets, (1) JUET contact database of 50 subjects having 10 templates each and (2) IITD contactless dataset of 240 subjects with 5 templates each. The proposed method outperforms other approaches with the best 0.31% of EER.