Face anti-spoofing, which aims to prevent the attacks of widely-used face recognition systems, is highly related to personal privacy and property security. However, existing benchmarks on face anti-spoofing mainly focus on RGB images, further challenged by consistently developed 3D high-fidelity (HiFi) masks. To facilitate the research on multimodal face anti-spoofing, we construct the HyperSpectral Face Anti-Spoofing (HySpeFAS) dataset. We introduce the newly-developed snapshot spectral imaging (SSI) technology to capture real and spoof faces, as well as identify unknown HiFi masks. Specifically, hyperspectral images (HSIs) acquired by SSI sensor contain rich information about the chemical composition of the targets, which can be used to effectively distinguish live human skin and various spoof materials. The HySpeFAS dataset contains 22,368 multimodal images (i.e., RGB, SSI, HSI) of 17 live subjects and 60 spoof subjects. Moreover, extensive experiments with baseline deep learning models validate the special features of the SSI images and the potential of SSI in FAS. By publishing the dataset as well as the baseline models, we encourage the community to foster the algorithm study associated with hyperspectral images and the development of SSI-equipped intelligent systems.

Snapshot spectral imaging technology based on the principle of compressed sensing can introduce additional spectral information and solve the problem of inadequate performance of existing optical sensors in dealing with high-fidelity masks. However, using compressed sensing for spectral image reconstruction requires a large amount of computational resources and has limited accuracy, making it the greatest obstacle for the practical application of snapshot spectral imaging technology. In this paper, we propose a new paradigm for Face Anti-Spoofing (FAS) based on snapshot spectral imaging technology. Our method does not require the reconstruction of compressive sensing spectral images, but instead directly treats the snapshot spectral imaging hardware as an optical encoder and designs the end-to-end Snapshot Spectral Imaging Network (SSINet) to decode the optical encoding results to obtain FAS results, which constitutes a software-hardware integrated FAS method. Experimental results show that our method not only surpasses previous state-of-the-art methods based on RGB images, but also performs the better than conventional spectral reconstruction methods in both efficiency and accuracy, with a nearly thousand-fold increase in FAS speed and an Average Classification Error Rate (ACER) decrease of 12.29%.

Cloud workflow scheduling (CWS) is critical for meeting user's high performance expectations in large-scale data processing and computing applications. CWS is known to be NP-hard and needs advanced scheduling techniques. Evolutionary algorithm and heuristic-based search techniques have gained massive popularity in addressing CWS, yet they either suffer from expensive computational cost or heavily rely on domain-specific experiences, which limit their practical applications. Bearing this in mind, we develop a novel evolutionary multi-task optimization framework to tackle a group of constrained CWS tasks simultaneously with the aid of adaptive cross-task problem-solving knowledge transfer. In particular, two collaborative knowledge exchange strategies, namely, constraint-free archive strategy and cross-task evolution strategy, are devised to extract useful building blocks from foreign tasks to boost the search efficiency. Further, to leverage the cooperative effects of both strategies, we develop an adaptive switching mechanism such that appropriate knowledge transfer strategies are learned automatically according to the population evolution status. Extensive experiments are conducted on real-world applications under various conditions, the comparison results show that our proposal delivers higher quality schedules than the state-of-the-art competitors in most cases.