Facial expressions play a significant role in the expression of emotional states, such as fear, surprise, and happiness in humans and other animals. The current systems for recognizing animal facial expression model in Non-human primates (NHPs) are currently limited to manual decoding of the facial muscles and observations, which is biased, time-consuming and requires a long training process and certification. The main objective of this work is to establish a computational framework for facial recognition systems for automatic recognition NHP facial expressions from standard video recordings with minimal assumptions. The suggested technology consists of: 1)a tailored facial image registration for NHPs; 2)a two-layers unsupervised clustering algorithm that forms an ordered dictionary of facial images for different facial segments; 3)extract dynamical temporal-spectral features;, and recognize dynamic facial expressions. The feasibility of the methods was verified using video recordings of an NHP under various behavioral conditions, recognizing typical NHP facial expressions in the wild. The results were compared to three human experts, and show an agreement of more than 82%. This work is the first attempt for efficient automatic recognition of facial expressions in NHPs using minimal assumptions about the physiology of facial expressions.