Abstract
Extracting a palm region with fixed location from an input hand image is a crucial task for palmprint recognition to realize reliable person authentication under unconstrained conditions. A palm region can be extracted from the fixed position using the gaps between fingers. Hence, an accurate and robust hand segmentation method is indispensable to extract a palm region from an image with complex background taken under various environments. This paper proposes a hand segmentation method for contactless palmprint recognition. The proposed method employs a new CNN architecture consisting of an encoder-decoder model of CNN with a pyramid pooling module. Through a set of experiments using a hand image dataset, we demonstrate that the proposed method exhibits efficient performance on hand segmentation.
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