Face recognition finds its place in a large number of applications. They occur in different contexts related to security, entertainment or Internet applications. Reliable face recognition is still a great challenge to computer vision and pattern recognition researchers, and new algorithms need to be evaluated on relevant databases. The publicly available IV2 database allows monomodal and multimodal experiments using face data. Known variabilities, that are critical for the performance of the biometric systems (such as pose, expression, illumination and quality) are present. The face and subface data that are acquired in this database are: 2D audio-video talking-face sequences, 2D stereoscopic data acquired with two pairs of synchronized cameras, 3D facial data acquired with a laser scanner, and iris images acquired with a portable infrared camera. The IV2 database is designed for monomodal and multimodal experiments. The quality of the acquired data is of great importance. Therefore as a first step, and in order to better evaluate the quality of the data, a first internal evaluation was conducted. Only a small amount of the total acquired data was used for this evaluation: 2D still images, 3D scans and iris images. First results show the interest of this database. In parallel to the research algorithms, open-source reference systems were also run for baseline comparisons.