Fingerprints are widely used for personal identification due to the high level of uniqueness exhibited by the characteristics of the ridge. Prominent applications include access control for devices, services, and buildings (e.g., smartphones, ATMs, and customs control). Most fingerprint acquisitions today rely on touch sensors because fine-grained fingerprint details, such as minutiae, are challenging to capture from moving images using a camera. Real-time video streams further complicate this process due to variations in brightness and background, making it difficult to focus on the finger’s surface and capture fingerprint details effectively. In addition, scale differences between the sampled fingerprint and the template can affect the accuracy of identity verification. In this work in progress, we propose a pipeline that integrates a fine-tuned version of YOLOv11s, to capture fingerprints from video in real-time. The proposed algorithm segments the distal phalanges of visible fingers, applies enhancement methods, extracts fingerprint features, and computes a feature-existence score for fingerprint matching and further HCI interaction. This score determines whether the fingerprint is sufficient for identity verification. We tested our proposal using validation images captured from HD videos with a 50 MP mobile phone camera, successfully segmenting distal phalanges located up to 120 cm. Also, our approach was validated on the public ISPFDv2 dataset comprising 16, 800 fingerphotos, achieving failure-to-acquire (FTA) rates below \(3.42\%\) on the first attempt and \(0.08\%\) on the fifth, highlighting the method’s potential for reliable fingerphoto segmentation.