Non-contact methods for monitoring respiration face limitations when it comes to selecting the chest region of interest. The semi-automatic method, which requires the user to select the chest region in the first frame, is not suitable for real-time applications. The automatic method, which tracks the face first and then detects the chest region based on the face's position, can be inaccurate if the face is not visible or is rotated. Moreover, using the face region to track the chest region can under-utilize camera pixels since the face is not essential for monitoring respiration. This approach may adversely affect the quality of the respiration signal being measured. To address these issues, we propose a face-free chest detection model based on Convolutional Neural Networks. Our model enhances the measured non-contact respiration signal quality and utilizes more pixels for the chest region alone. In our quantitative study, we demonstrate that our method outperforms traditional methods that require the presence of the face. This approach offers potential benefits for real-time, non-contact respiration monitoring applicationsClinical relevance- This work enhances the performance of non-contact respiration monitoring techniques by precisely detecting the chest region without the need of face in it through a CNN-based model. The use of the CNN-based chest detection model also enhances the real-time monitoring capabilities of non-contact respiration monitoring techniques.