Feature Component-Based Extreme Learning Machines for Finger Vein Recognition

This paper proposes an efficient finger vein recognition system, in which a variant of the original ensemble extreme learning machine (ELM) called the feature component-based ELMs (FC-ELMs) designed to utilize the characteristics of the features, is introduced to improve the recognition accuracy and stability and to substantially reduce the number of hidden nodes. For feature extraction, an explicit guided filter is proposed to extract the eight block-based directional features from the high-quality finger vein contours obtained from noisy, non-uniform, low-contrast finger vein images without introducing any segmentation process. An FC-ELMs consist of eight single ELMs, each trained with a block feature with a pre-defined direction to enhance the robustness against variation of the finger vein images, and an output layer to combine the outputs of the eight ELMs. For the structured training of the vein patterns, the FC-ELMs are designed to first train small differences between patterns with the same angle and then to aggregate the differences at the output layer. Each ELM can easily learn lower-complexity patterns with a smaller network and the matching accuracy can also be improved, due to the less complex boundaries required for each ELM. We also designed the ensemble FC-ELMs to provide the matching system with stability. For the dataset considered, the experimental results show that the proposed system is able to generate clearer vein contours and has good matching performance with an accuracy of 99.53 % and speed of 0.87 ms per image.