  With the growing availability of databases for face presentation attack
detection, researchers are increasingly focusing on video-based face
anti-spoofing methods that involve hundreds to thousands of images for training
the models. However, there is currently no clear consensus on the optimal
number of frames in a video to improve face spoofing detection. Inspired by the
visual saliency theory, we present a video summarization method for face
anti-spoofing detection that aims to enhance the performance and efficiency of
deep learning models by leveraging visual saliency. In particular, saliency
information is extracted from the differences between the Laplacian and Wiener
filter outputs of the source images, enabling identification of the most
visually salient regions within each frame. Subsequently, the source images are
decomposed into base and detail images, enhancing the representation of the
most important information. Weighting maps are then computed based on the
saliency information, indicating the importance of each pixel in the image. By
linearly combining the base and detail images using the weighting maps, the
method fuses the source images to create a single representative image that
summarizes the entire video. The key contribution of the proposed method lies
in demonstrating how visual saliency can be used as a data-centric approach to
improve the performance and efficiency for face presentation attack detection.
By focusing on the most salient images or regions within the images, a more
representative and diverse training set can be created, potentially leading to
more effective models. To validate the method's effectiveness, a simple CNN-RNN
deep learning architecture was used, and the experimental results showcased
state-of-the-art performance on five challenging face anti-spoofing datasets
