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Expressive behaviors conveyed during daily interactions are difficult to determine, because they often consist of a blend of different emotions. The complexity in expressive human communication is an important challenge to build and evaluate automatic systems that can reliably predict emotions. Emotion recognition systems are often trained with limited databases, where the emotions are either elicited or recorded by actors. These approaches do not necessarily reflect real emotions, creating a mismatch when the same emotion recognition systems are applied to practical applications. Developing rich emotional databases that reflect the complexity in the externalization of emotion is an important step to build better models to recognize emotions. This study presents the MSP-Face database, a natural audiovisual database obtained from video-sharing websites, where multiple individuals discuss various topics expressing their opinions and experiences. The natural recordings convey a broad range of emotions that are difficult to obtain with other alternative data collection protocols. A feature of the corpus is the addition of two sets. The first set includes videos that have been annotated with emotional labels using a crowd-sourcing protocol (9,370 recordings – 24 hrs, 41 m). The second set includes similar videos without emotional labels (17,955 recordings – 45 hrs, 57 m), offering the perfect infrastructure to explore semi-supervised and unsupervised machine-learning algorithms on natural emotional videos. This study describes the process of collecting and annotating the corpus. It also provides baselines over this new database using unimodal (audio, video) and multimodal emotional recognition systems.