Biometric presentation attack detection: beyond the visible spectrum

The increased need for unattended authentication in multiple scenarios has motivated a wide deployment of biometric systems in the last few years. This has in turn led to the disclosure of security concerns specifically related to biometric systems. Among them, presentation attacks (PAs, i.e., attempts to log into the system with a fake biometric characteristic or presentation attack instrument) pose a severe threat to the security of the system: any person could eventually fabricate or order a gummy finger or face mask to impersonate someone else. In this context, we present a novel fingerprint presentation attack detection (PAD) scheme based on i) a new capture device able to acquire images within the short wave infrared (SWIR) spectrum, and i i) an in-depth analysis of several state-of-theart techniques based on both handcrafted and deep learning features. The approach is evaluated on a database comprising over 4700 samples, stemming from 562 different subjects and 35 different presentation attack instrument (PAI) species. The results show the soundness of the proposed approach with a detection equal error rate (D-EER) as low as 1.35% even in a realistic scenario where five different PAI species are considered only for testing purposes (i.e., unknown attacks

Multi-set canonical correlation analysis for 3D abnormal gait behaviour recognition based on virtual sample generation

Small sample dataset and two-dimensional (2D) approach are challenges to vision-based abnormal gait behaviour recognition (AGBR). The lack of three-dimensional (3D) structure of the human body causes 2D based methods to be limited in abnormal gait virtual sample generation (VSG). In this paper, 3D AGBR based on VSG and multi-set canonical correlation analysis (3D-AGRBMCCA) is proposed. First, the unstructured point cloud data of gait are obtained by using a structured light sensor. A 3D parametric body model is then deformed to fit the point cloud data, both in shape and posture. The features of point cloud data are then converted to a high-level structured representation of the body. The parametric body model is used for VSG based on the estimated body pose and shape data. Symmetry virtual samples, pose-perturbation virtual samples and various body-shape virtual samples with multi-views are generated to extend the training samples. The spatial-temporal features of the abnormal gait behaviour from different views, body pose and shape parameters are then extracted by convolutional neural network based Long Short-Term Memory model network. These are projected onto a uniform pattern space using deep learning based multi-set canonical correlation analysis. Experiments on four publicly available datasets show the proposed system performs well under various conditions

In-Vivo Skin Capacitive Image Classification Using AlexNet Convolution Neural Network

© 2018 IEEE. Skin water content is very important for its cosmetic properties and its barrier functions, however, to measure it is very difficult. We have recently developed a novel hand-held probe for in-vivo skin hydration imaging based on the capacitance measurement principle. It is more repeatable, reproducible and easier to calibrate than the existing commercial devices. Our latest research is to assess the performance of deep learning in in-vivo skin capacitive image analysis using AlexNet model. As we know the AlexNet model can be used for image classification and recognition with high accuracy. Our object is to design a model to classify more than one specific features, i.e. not just the one with highest probability. We trained the image classifier using the pretrained model to implement the specific feature extraction, prediction and classification specifically for the skin hydration level, skin damage level and gender. There are over 1000 skin images which are measured by two experiments: repeatability of different instruments in in-vivo skin measurement; and skin damage measurements by different instruments. The objective of the research has been divided into three parts: feature extraction implementation using the pretrained model AlexNet; accuracy assessment of the model; further improve the system for multiple features classification. The image classification programme shows a good result which has accuracy 0.98, and the test images were classified correctly comparing with the experimental results of skin hydration, skin damaged level and the gender of the volunteers

Multi-Level Pooling Model for Fingerprint-Based Gender Classification

It has been widely reported that CNN (Convolutional Neural Network) has shown satisfactory results in classifying images. The strength of CNN lies in the type and the number of layers that construct it. However, the most apparent drawbacks of CNN are the requirement for a large labeled dataset and its lengthy training time. Although datasets are available, labeling that data is a significant problem. This work mimics the CNN model but only utilizes its pooling layers. The novelty of this model is removing convolution layers and directly processing fingerprint images using pooling layers. Three pooling layer models, namely maximum pooling, average pooling, and minimum pooling, are used to generate fingerprint features to classify their owner gender. These pooling layers are arranged consecutively up to eight levels. Removing convolution layers makes the process straightforward, and the computation is much faster. This study utilized 200 fingerprint datasets from the NIST (National Institute of Standards and Technology), with male and female fingerprints of 100 samples each. The extracted features were then classified using K-NN (K-Nearest Neighbors) algorithm. The proposed method resulted in an accuracy of 61% to 71.5% or an average of 66.25%