With the growing use of biometric authentication systems in the recent years, spoof fingerprint detection has become increasingly important. In this paper, we use convolutional neural networks (CNNs) for fingerprint liveness detection. Our system is evaluated on the data sets used in the liveness detection competition of the years 2009, 2011, and 2013, which comprises almost 50 000 real and fake fingerprints images. We compare four different models: two CNNs pretrained on natural images and fine-tuned with the fingerprint images, CNN with random weights, and a classical local binary pattern approach. We show that pretrained CNNs can yield the state-of-the-art results with no need for architecture or hyperparameter selection. Data set augmentation is used to increase the classifiers performance, not only for deep architectures but also for shallow ones. We also report good accuracy on very small training sets (400 samples) using these large pretrained networks. Our best model achieves an overall rate of 97.1% of correctly classified samples-a relative improvement of 16% in test error when compared with the best previously published results. This model won the first prize in the fingerprint liveness detection competition 2015 with an overall accuracy of 95.5%.

This paper presents an array-based node-oriented structure for the max-tree representation, which allows direct access and flexible manipulation of its nodes, and is more suitable for OpenMP parallel processing. The proposed structure is based on two arrays called node array (NA), which stores attributes of the nodes, and node index (NI), which indicates the node that each pixel belongs to. We compare it with the pixel-oriented max-tree representation based on a parent array (parent) and an ordering array (S) that allows tree traversals. We show that our max-tree representation requires less memory when the ratio between the number of image pixels and max-tree nodes is greater than 1.6, which is often the case. It is more flexible, and can compute some attributes, such as height and dynamics, with a complexity linear on the number of max-tree nodes instead of the number of image pixels. In our experiments our structure computed the height attribute on average 11.4 faster than the parent/S representation. Also, for a single area-open filter, the sequential implementation of our structure is on average 1.14 times slower and the parallel implementation in a 4-core CPU is 1.2 times faster than the parent/S structure. For an area-open filter followed by the hmax filter, our sequential implementation is 1.34 times faster and our parallel implementation is 2.32 times faster than the parent/S structure.

The Max-Tree is an efficient data structure that represents all connected components resulting from all possible image upper threshold values. Usually, most of its nodes represent irrelevant extrema, i.e. noise, or small variations of a connected component. This paper proposes the Maximal Max-Tree Simplification (MMS) filter with a normalized threshold criterion (MMS-T) and a Maximally Stable Extremal Regions (MSER) criterion (MMS-MSER) and a methodology to apply them using the Extinction filter We show that after applying our simplification methodology which sets the number of maxima in the image, the number of Max-Tree nodes is at most twice this number. Two applications of the proposed methodology are illustrated.

This work consists in the study, development and implementation of a toolbox for image processing using the Python language and the numerical Python package. This set has "open source" distribution and is adequate for multidimensional mathematical processing. Python is a modern and well projected language, interpreted, "very-high-level", object oriented and extremely portable, apart from being suitable for rapid application development (RAD). The system is generated using the methodology of the Adesso project for construction of scientific software. This environment is useful in education, research and development of final applications.