Latent fingerprints are important for identifying criminal suspects. However,
recognizing a latent fingerprint in a collection of reference fingerprints
remains a challenge. Most, if not all, of existing methods would extract
representation features of each fingerprint independently and then compare the
similarity of these representation features for recognition in a different
process. Without the supervision of similarity for the feature extraction
process, the extracted representation features are hard to optimally reflect
the similarity of the two compared fingerprints which is the base for matching
decision making. In this paper, we propose a new scheme that can model the
pair-relationship of two fingerprints directly as the similarity feature for
recognition. The pair-relationship is modeled by a hybrid deep network which
can handle the difficulties of random sizes and corrupted areas of latent
fingerprints. Experimental results on two databases show that the proposed
method outperforms the state of the art.

研究使用混合深度网络建立两个指纹间相似性特征直接模型的方法，得到的结果在两个数据集上均优于现有技术。

潜指纹识别的对偶关系建模

Pair-Relationship Modeling for Latent Fingerprint Recognition

We use the scattering network as a generic and fixed ini-tialization of the
first layers of a supervised hybrid deep network. We show that early layers do
not necessarily need to be learned, providing the best results to-date with
pre-defined representations while being competitive with Deep CNNs. Using a
shallow cascade of 1 x 1 convolutions, which encodes scattering coefficients
that correspond to spatial windows of very small sizes, permits to obtain
AlexNet accuracy on the imagenet ILSVRC2012. We demonstrate that this local
encoding explicitly learns invariance w.r.t. rotations. Combining scattering
networks with a modern ResNet, we achieve a single-crop top 5 error of 11.4% on
imagenet ILSVRC2012, comparable to the Resnet-18 architecture, while utilizing
only 10 layers. We also find that hybrid architectures can yield excellent
performance in the small sample regime, exceeding their end-to-end
counterparts, through their ability to incorporate geometrical priors. We
demonstrate this on subsets of the CIFAR-10 dataset and on the STL-10 dataset.

使用散射网络作为监督式混合深度网络的头几层的一般和固定初始化，结合局部编码可以达到与 CNNs 竞争的最佳结果，还可以在小样本情况下通过几何先验获得更好性能。