哈佛大学的霍顿图书馆于2006年12月份成功实现了其手稿卡片目录的电子化。现在霍顿图书馆手稿藏品目录在哈佛的在线目录（hollis．harvard．edu）上提供网上查询，在哈佛查询指引数据库（oasis．harvard．edu）或美国研究图书馆组织（RL...哈佛大学的霍顿图书馆于2006年12月份成功实现了其手稿卡片目录的电子化。现在霍顿图书馆手稿藏品目录在哈佛的在线目录（hollis．harvard．edu）上提供网上查询，在哈佛查询指引数据库（oasis．harvard．edu）或美国研究图书馆组织（RLG）的Archives Grid可以得到检索方面的帮助。这个为期5年的项目由哈佛大学数字图书馆计划资助，并由哈佛学院图书馆（Harvard College Library）提供配套资金。展开更多
With the rapid growth in fingerprint databases, it has become necessary to develop excellent fingerprint indexing to achieve efficiency and accuracy. Fingerprint indexing has been widely studied with real-valued featu...With the rapid growth in fingerprint databases, it has become necessary to develop excellent fingerprint indexing to achieve efficiency and accuracy. Fingerprint indexing has been widely studied with real-valued features,but few studies focus on binary feature representation, which is more suitable to identify fingerprints efficiently in large-scale fingerprint databases. In this study, we propose a deep compact binary minutia cylinder code（DCBMCC）as an effective and discriminative feature representation for fingerprint indexing. Specifically, the minutia cylinder code（MCC）, as the state-of-the-art fingerprint representation, is analyzed and its shortcomings are revealed.Accordingly, we propose a novel fingerprint indexing method based on deep neural networks to learn DCBMCC.Our novel network restricts the penultimate layer to directly output binary codes. Moreover, we incorporate independence, balance, quantization-loss-minimum, and similarity-preservation properties in this learning process.Eventually, a multi-index hashing（MIH） based fingerprint indexing scheme further speeds up the exact search in the Hamming space by building multiple hash tables on binary code substrings. Furthermore, numerous experiments on public databases show that the proposed approach is an outstanding fingerprint indexing method since it has an extremely small error rate with a very low penetration rate.展开更多
文摘哈佛大学的霍顿图书馆于2006年12月份成功实现了其手稿卡片目录的电子化。现在霍顿图书馆手稿藏品目录在哈佛的在线目录（hollis．harvard．edu）上提供网上查询，在哈佛查询指引数据库（oasis．harvard．edu）或美国研究图书馆组织（RLG）的Archives Grid可以得到检索方面的帮助。这个为期5年的项目由哈佛大学数字图书馆计划资助，并由哈佛学院图书馆（Harvard College Library）提供配套资金。
基金supported by the National Natural Science Foundation of China(Nos.11331012,11571014,and 11731013)
文摘With the rapid growth in fingerprint databases, it has become necessary to develop excellent fingerprint indexing to achieve efficiency and accuracy. Fingerprint indexing has been widely studied with real-valued features,but few studies focus on binary feature representation, which is more suitable to identify fingerprints efficiently in large-scale fingerprint databases. In this study, we propose a deep compact binary minutia cylinder code（DCBMCC）as an effective and discriminative feature representation for fingerprint indexing. Specifically, the minutia cylinder code（MCC）, as the state-of-the-art fingerprint representation, is analyzed and its shortcomings are revealed.Accordingly, we propose a novel fingerprint indexing method based on deep neural networks to learn DCBMCC.Our novel network restricts the penultimate layer to directly output binary codes. Moreover, we incorporate independence, balance, quantization-loss-minimum, and similarity-preservation properties in this learning process.Eventually, a multi-index hashing（MIH） based fingerprint indexing scheme further speeds up the exact search in the Hamming space by building multiple hash tables on binary code substrings. Furthermore, numerous experiments on public databases show that the proposed approach is an outstanding fingerprint indexing method since it has an extremely small error rate with a very low penetration rate.
