  Face recognition technology is widely used in the financial field, and
various types of liveness attack behaviors need to be addressed. Existing
liveness detection algorithms are trained on specific training datasets and
tested on testing datasets, but their performance and robustness in
transferring to unseen datasets are relatively poor. To tackle this issue, we
propose a face liveness detection method based on image-text pairs and
contrastive learning, dividing liveness attack problems in the financial field
into eight categories and using text information to describe the images of
these eight types of attacks. The text encoder and image encoder are used to
extract feature vector representations for the classification description text
and face images, respectively. By maximizing the similarity of positive samples
and minimizing the similarity of negative samples, the model learns shared
representations between images and texts. The proposed method is capable of
effectively detecting specific liveness attack behaviors in certain scenarios,
such as those occurring in dark environments or involving the tampering of ID
card photos. Additionally, it is also effective in detecting traditional
liveness attack methods, such as printing photo attacks and screen remake
attacks. The zero-shot capabilities of face liveness detection on five public
datasets, including NUAA, CASIA-FASD, Replay-Attack, OULU-NPU and MSU-MFSD also
reaches the level of commercial algorithms. The detection capability of
proposed algorithm was verified on 5 types of testing datasets, and the results
show that the method outperformed commercial algorithms, and the detection
rates reached 100% on multiple datasets. Demonstrating the effectiveness and
robustness of introducing image-text pairs and contrastive learning into
liveness detection tasks as proposed in this paper.
