Facial acne is a prevalent skin disease worldwide. Misjudging the severity of its condition can lead to permanent facial damage or negatively impact patients’ self-esteem. With the rapid advancements in computer vision techniques, automated methods for detecting or grading facial acne in digital images are attracting growing attention. However, in clinical practice, multi-view (left, front, and right) face images used for accurate diagnosis pose a significant challenge due to redundant acne detection on the overlapped image regions that can mislead diagnostic results. In this paper, we propose a novel facial acne detection framework to detect various kinds of acne for accurate acne diagnosis or prognosis prediction. Specifically, multi-view facial images are first stitched by aligning key facial feature points, and then the proposed acne detection network YoloV5-Acne is then applied to the stitched images for patient-level acne detection. Experimental results on a collected multi-view face image dataset (ACNE-Shanghai) and a public acne dataset (ACNE04) validate the effectiveness of the proposed method on various evaluation metrics. The code and the dataset will be released for the research purpose once the paper gets published.