Abstract: This paper introduces an approach to acoustic channel identification rooted in a geometric model that accounts for human factors. This method holds promise for applications in active noise control technology, specifically addressing the challenge of maintaining optimal noise reduction performance despite the changes in human posture. The approach involves extracting human factor parameters from the point cloud data acquired through a depth camera, which are then used to construct a precise geometric model. This model is utilized to predict the transfer function matrix of the acoustic channel through simulation and artificial neural networks. The results demonstrate that the geometric model incorporating human factor parameters yields noise reduction performance comparable to that of the traditional point cloud models, making it an attractive option for parametric scanning simulations. Furthermore, experimental verification also validates the accuracy of the simulated results and the feasibility of the proposed approach. The findings show the significant impact of changing human posture on the complexity and variability of the acoustic channel, emphasizing the importance of the posture effects in real-world applications.