Deep Learning in Biometrics: A Survey

Alberto BOTANA LÓPEZ
2019 Advances in Distributed Computing and Artificial Intelligence Journal  
Deep learning; biometrics; fingerprint; ocular recognition; convolutional neural networks; electrocardiogram Deep learning has been established in the last few years as the gold standard for data processing, achieving peak performance in image, text and audio understanding. At the same time, digital security is of utmost importance in this day and age, where everyone could get into our personal devices like cellphones or laptops, where we store our most valuable information. One of the possible
more » ... ways to prevent this is via advanced and personalized security: biometrics. In this survey, it is considered how the scientific advances in the field of deep learning are applied to biometrics in order to enhance the protection of our data. Firstly, a study will be conducted on tackling ocular identification of twins using deep learning. Then, an improved method for avoiding fingerprint spoofing will be presented, thus solving this method's biggest issue. Finally, a brand new method for biometric identification is proposed based on the usage of the user's electrocardiogram. On every one of these methods, the results manage to top the standard alternative performances. Ediciones Universidad de Salamancacc by nc dc 2.1. Proposed model When using SNNs (Koch et al., 2015), a new kind of loss function must be used in order to join both of the networks and compute the similarity between the feature output vectors obtained from each of the networks. This function, named Contrastive Loss Function (CLF) is described by:
doi:10.14201/adcaij2019841932 fatcat:goenvskglzamdohj5yhahq2mzq