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Nonverbal communication, such as body language, facial expressions, and hand gestures, is crucial to human communication as it conveys more information about emotions and attitudes than spoken words. However, individuals who are blind or have low-vision (BLV) may not have access to this method of communication, leading to asymmetry in conversations. Developing systems to recognize nonverbal communication cues (NVCs) for the BLV community would enhance communication and understanding for both parties. This paper focuses on developing a multimodal computer vision system to recognize and detect NVCs. To accomplish our objective, we are collecting a dataset focused on nonverbal communication cues. Here, we propose a baseline model for recognizing NVCs and present initial results on the Aff-Wild2 dataset. Our baseline model achieved an accuracy of 68% and a F1-Score of 64% on the Aff-Wild2 validation set, making it comparable with previous state of the art results. Furthermore, we discuss the various challenges associated with NVC recognition as well as the limitations of our current work.