# Evaluation of a Vein Biometric Recognition System on an Ordinary Smartphone
## 1 Introduction  
Biometrics refers to measurements of a biological trait [1] and their employment for individual recognition. Involuntarily, we apply biometric recognition in many daily situations. For example, when someone who is not in our contact list calls us by phone, we try to associate the voice we hear with the people we know. If the voice matches one of our memories, we quickly recognize the person. Biometric recognition systems are automated methods to recognize an individual. There are two phases in a biometric recognition system [1]. In the first phase, enrollment, the individual registers his/her biometric data, which are stored in the system (normally, in a database). The registered data are known as template. In the second phase,matching, the individual is recognized from new biometric data. The second phase is carried out as many times as the individual has to be recognized. A biometric recognition system is composed of four stages: acquisition, preprocessing, feature extraction, template storage, and matching. Samples of biometric traits are obtained during the acquisition stage, in which sensors get raw data. These data are preprocessed by applying filters and extracting regions of interest, among others. The feature extraction stage is applied to generate a representation of the samples that facilitates the generation and storage of templates at enrollment and the comparison with them at matching. The matching result (also known as the score) shows how similar templates are. Comparisons can be split into genuine and impostor. Genuine comparisons are computed between samples from the same individual, while impostor comparisons are computed between samples from different individuals. False non-match rate (FNMR) and false match rate (FMR) are usual indicators for the evaluation of the authentication performance. They are also known as false rejection rate (FRR) and false acceptance rate (FAR), respectively. FNMR is determined by the number of genuine comparisons with a similarity score smaller than the authentication threshold. On the other hand, FMR is determined by the number of impostor comparisons with a score greater than the threshold. From these indicators, equal error rate (EER) is the value where FNMR and FMR coincide. Nowadays, biometric recognition systems are employed in many application fields for access control in electronic commerce, banking, tourism, migration, offices, hospitals, cloud, etc. The advantages of biometrics over passwords or identity cards is that biometric data do not need to be remembered and the risk of theft is reduced because biological measurements are intrinsic to individuals. Biometric recognition systems became very popular after they were introduced in daily life devices (laptops, tablets, or smartphones) to protect personal data and carry out secure payments. The introduction of biometric recognition systems in mobile phones was in 2004 (almost twenty years ago) in the Pantech GI100. In 2007, fingerprint recognition was included in the Toshiba G500, consolidating the use of fingerprint sensors in mobile phones. The acceptance of fingerprint recognition in the smartphone market started in 2013 with the inclusion of the Touch-ID system in the iPhone $5. As a reaction to the Apple action, Android included fingerprint recognition in the Samsung Galaxy $5 in 2014. In the last years, the number of smartphones with fingerprint recognition has increased exponentially. Meanwhile, the development of facial recognition technologies has gained importance over the years. In 2012, Google registered a patent for facial smartphone unlocking and integrated it in Android 4.0 systems. Similar to the Touch-ID system, Apple included the Face-ID system in the iPhone X in 2017. The acquisition in Face-ID (like in other facial recognition systems) employs an infrared dot projector module in addition to the camera. Biometric recognition based on vascular traits is a relatively recent technique that has not yet been sufficiently studied and exploited [2].