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A morph is an image of an ambiguous subject generated by combining multiple individuals. The morphed image can be submitted to a facial recognition system and erroneously verified with the contributing bad actors. When submitted as a passport image, a morphed face poses a national security threat because a passport can then be shared between the individuals. As morphed images become easier to generate, it is vital that the research community expands available datasets in order to contentiously improve current technology. Children are a challenging paradigm for facial recognition systems and morphing children takes advantage of this disparity. In this paper, we morph juvenile faces in order to create a unique, high-quality dataset to challenge FRS. To the best of our knowledge, this is the first study on the generation and evaluation of juvenile morphed faces. The evaluation of the generated morphed juvenile dataset is performed in terms of vulnerability analysis and presentation attack error rates.