# Evaluation of a Vein Biometric Recognition System on an Ordinary Smartphone
## 2 Related Work  
A relevant step in vein recognition is the acquisition of samples. High-quality images reduce the complexity of image preprocessing and increase recognition accuracy. The acquisition process is primarily based on two properties [1]: (1) an inoffensive near-infrared (NIR) light that can enter the skin with a radiation wavelength larger than visible light (around 750-1000 nm), and (2) the presence of hemoglobin in the bloodstream, which absorbs this radiation. Hemoglobin can be classified as oxygenated and deoxygenated, depending on whether it contains dioxygen or not. Deoxygenated hemoglobin shows a maximum absorption at wavelengths of 760 nm, while oxygenated hemoglobin shows a higher absorption at 800 nm [20]. Vein acquisition devices are usually composed of a NIR illumination source and a camera. Based on their positions, two different methods are used for the acquisition: reflection and refraction (or transmission), which are optical phenomena that occur when a ray of light propagating through a medium encounters a separating surface of another medium. During reflection, part of the incident light changes direction and continues to propagate in the original medium. Therefore, images are obtained using the light reflected by the tissues and veins. In this case, the camera and the NIR light source are on the same side and the biometric sample is in front of them. During refraction, part of the light is transmitted to the other medium. So, in the case of transmission, the refracted light is captured by a camera in front of the NIR light source and the biometric sample is placed between the light source and the camera. In the refraction method, the light has to pass through the human tissue until the camera captures it. Therefore, infrared light should have higher intensity. Besides, due to the locations of the infrared light source and the camera, the acquisition device is bulkier. On the contrary, the reflection method allows a more compact design with a lower power consumption. In this work, we focus on the reflection-based acquisition because it is more suitable for smartphones. In the literature, there are several proposals based on the reflection method to acquire veins in the infrared spectrum from the palms, dorsal hands, fingers, and wrists. These proposals are summarized in the following. For palm veins, the acquisition prototype described in [8] was used to acquire the VERA PalmVein database. The prototype contains a CCD camera and twenty 940-nm NIR LEDs placed on the edges of a printed circuit board (PCB). In addition, it includes a 920-nm high-pass filter to reduce ambient light and an ultrasound distance sensor to turn off the acquisition when the hand is not placed at an adequate distance. A graphical user interface (GUI), which allows for checking whether the palm is centered in the image and the infrared light is adequate, guides the acquisition. The samples were acquired inside a building. The database was evaluated in [9] considering the region of interest (ROI) of the images. The EER obtained was 9.86% for images without enhancement and 6.25% for images with enhancement. The acquisition prototype described in [10] was used for dorsal hands. It employed the camera of a Nexus 5 smartphone modified to remove the infrared blocking filter. As the infrared lighting source, a structure was created with sixteen 850-nm NIR LEDs placed in a circle with the smartphone camera in its center. In addition, a 780-nm high-pass filter was used to reduce the influence of ambient light. Each LED was controlled individually from an app developed for the acquisition that interacted with an Arduino-based control board. The acquisition was carried out inside a car and inside a building. The extraction of the ROI was carried out manually and the images were enhanced. The recognition performance evaluation showed that the best EER value was 4.13% and the worst EER was 24.30%.