Optical fingerprint liveness detection plays a crucial role in preventing spoofing attacks on fingerprint recognition systems. Existing deep learning-based methods require large amounts of labeled data, while fingerprint image acquisition remains challenging. A few-shot optical fingerprint liveness detection method, featuring the fusion of spatial and frequency domain features, has been proposed for few-shot scenarios. Performance in liveness detection under few-shot conditions is enhanced using a bidirectional cross-domain attention mechanism and a high-frequency enhancement factor. Experimental results demonstrate outstanding performance on two benchmark datasets. With only 10 samples, the average classification error rate (ACER) reaches as low as 0.21% and 0.45%, outperforming existing methods. Notably, excellent adaptability in cross-sensor detection is shown. The method expands the practical applications of fingerprint liveness detection technology.