The quality of iris images on mobile devices is significantly degraded due to hardware limitations and less constrained environments. Traditional iris recognition methods cannot achieve high identification rate using these low-quality images. To enhance the performance of mobile identification, we develop a deep feature fusion network that exploits the complementary information presented in iris and periocular regions. The proposed method first applies maxout units into the convolutional neural networks (CNNs) to generate a compact representation for each modality and then fuses the discriminative features of two modalities through a weighted concatenation. The parameters of convolutional filters and fusion weights are simultaneously learned to optimize the joint representation of iris and periocular biometrics. To promote the iris recognition research on mobile devices under near-infrared (NIR) illumination, we publicly release the CASIA-Iris-Mobile-V1.0 database, which in total includes 11 000 NIR iris images of both eyes from 630 Asians. It is the largest NIR mobile iris database as far as we know. On the newly built CASIA-Iris-M1-S3 data set, the proposed method achieves 0.60% equal error rate and 2.32% false non-match rate at false match rate $=10^{-5}$ , which are obviously better than unimodal biometrics as well as traditional fusion methods. Moreover, the proposed model requires much fewer storage spaces and computational resources than general CNNs.

Deep Feature Fusion for Iris and Periocular Biometrics on Mobile Devices

An end to end Deep Neural Network for iris segmentation in unconstrained scenarios.

Gait is a biometric trait that can allow user authentication, though it is classified as a “soft” one due to a certain lack in permanence and to sensibility to specific conditions. The earliest research relies on computer vision, especially applied in video surveillance. More recently, the spread of wearable sensors, especially those embedded in mobile devices, has spurred a different research line. In fact, they are able to capture the dynamics of the walking pattern through simpler one-dimensional signals. This capture modality can avoid some problems related to computer vision-based techniques but suffers from specific limitations. Related research is still in a less advanced phase with respect to other biometric traits. However, many factors - the promising results achieved so far, the increasing accuracy of sensors, the ubiquitous presence of mobile devices, and the low cost of related techniques - contribute to making this biometrics attractive and suggest continuing investigating. This survey provides interested readers with a reasoned and systematic overview of problems, approaches, and available benchmarks.

https://dl.acm.org/doi/pdf/10.1145/3340293

A Survey on Gait Recognition via Wearable Sensors

The prevalent commercial deployment of mobile biometrics as a robust authentication method on mobile devices has fueled increasingly scientific attention. Motivated by this, in this work we seek to provide insight on recent development in mobile biometrics. We present parallels and dissimilarities of mobile biometrics and classical biometrics, enumerate related strengths and challenges. Further we provide an overview of recent techniques in mobile biometrics, as well as application systems adopted by industry. Finally, we discuss open research problems in this field.

Hugo Proena

Papers

Results from MICHE II Mobile Iris CHallenge Evaluation II