Introduction: Emotion recognition through facial expressions is crucial for interpreting social cues. However, it is often influenced by biases, i.e., systematic recognition advantages for particular emotions. Nevertheless, these biases are inconsistently reported across studies, likely due to methodological variations which underline the necessity for a standardized approach. Traditional face morphing methods can create unnatural-looking stimuli, and may confound the interpretation of emotions. To address this issue, we here introduce Methods: We employed neutral-anchored morphing and neural-network-generated masks to reduce morphing artifacts and preserve the coherence of the depicted expressions. We validated our stimulus set by having participants rate each face on a 9-point scale ranging from angry to happy, assessing the perceived emotional intensity. Reliability was assessed through a two-week follow-up rating session with a subgroup of the same participants. Results: Discussion: By introducing a controlled and empirically evaluated stimulus set of morphed emotional faces,