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Description: We introduce a novel dataset of affective, semantic, and descriptive norms for all facial emojis. We gathered and examined subjective ratings from 138 German speakers along five essential dimensions: valence, arousal, familiarity, clarity, and visual complexity. Additionally, we provide absolute frequency counts of emoji use, drawn from an extensive Twitter corpus. Our results replicate the well-established quadratic relationship between arousal and valence of lexical items, also known for words. We also report associations among the variables: for example, subjective familiarity is strongly correlated with usage frequency, and positively associated with valence and clarity. To establish the meanings associated with face emojis, we asked for up to three descriptions for each emoji. Using this linguistic data, we computed vector embeddings for each emoji, enabling an exploration of their distribution within the semantic space. Our description based emoji vector embeddings not only capture typical meaning components of emojis, such as their valence, but also surpass simple definitions and direct emoji2vec models. If you use the data, please cite the paper: https://doi.org/10.3758/s13428-024-02444-x Please see this website for a more human-readable view: https://tscheffler.github.io/2024-Face-Emoji-Norming/home.html