A computer-implemented method for face liveness detection, the method comprising: training a first deep learning model based on general facial images, wherein each general facial image of the general facial images comprises a first label indicating whether a general facial image corresponding to the first label is a live facial image or a non-live facial image, the live facial image being collected by shooting a live face and the non-live facial image being collected by shooting a non-live face comprising at least one of a face picture or a face model; training a second deep learning model based on extracted facial images cropped from the general facial images, wherein each extracted facial image of the extracted facial images comprises a second label indicating whether an extracted facial image corresponding to the second label is a live facial image or a non-live facial image, wherein the general facial images include each an entire facial region and some unrelated regions and the respective extracted facial images exclude the unrelated regions and include at least an extracted entire or partial facial region, or the general facial images include each an entire facial region and exclude an unrelated region and the respective extracted facial images include only a partial facial region; performing face liveness detection on a first general facial image using the trained first deep learning model to obtain a first prediction score, PA, and using the trained second deep learning model to obtain a second prediction score, PB; generating a prediction score result as a sum (PA+PB) of the first prediction score and the second prediction score; and comparing the prediction score result with a threshold to determine a face liveness detection result for the first general facial image. training a first deep learning model based on general facial images, wherein each general facial image of the general facial images comprises a first label indicating whether a general facial image corresponding to the first label is a live facial image or a non-live facial image, the live facial image being collected by shooting a live face and the non-live facial image being collected by shooting a non-live face comprising at least one of a face picture or a face model; training a second deep learning model based on extracted facial images cropped from the general facial images, wherein each extracted facial image of the extracted facial images comprises a second label indicating whether an extracted facial image corresponding to the second label is a live facial image or a non-live facial image, wherein the general facial images include each an entire facial region and some unrelated regions and the respective extracted facial images exclude the unrelated regions and include at least an extracted entire or partial facial region, or the general facial images include each an entire facial region and exclude an unrelated region and the respective extracted facial images include only a partial facial region; the general facial images include each an entire facial region and some unrelated regions and the respective extracted facial images exclude the unrelated regions and include at least an extracted entire or partial facial region, or the general facial images include each an entire facial region and exclude an unrelated region and the respective extracted facial images include only a partial facial region; performing face liveness detection on a first general facial image using the trained first deep learning model to obtain a first prediction score, PA, and using the trained second deep learning model to obtain a second prediction score, PB; generating a prediction score result as a sum (PA+PB) of the first prediction score and the second prediction score; and comparing the prediction score result with a threshold to determine a face liveness detection result for the first general facial image.