Hydrogen is readily obtained from renewable and non‐renewable resources via water splitting by using thermal, electrical, photonic and biochemical energy. The major hydrogen production is generated from thermal energy through steam reforming/gasification of fossil fuel. As the commonly used non‐renewable resources will be depleted in the long run, there is great demand to utilize renewable energy resources for hydrogen production. Most of the renewable resources may be used to produce electricity for driving water splitting while challenges remain to improve cost‐effectiveness. As the most abundant energy resource, the direct conversion of solar energy to hydrogen is considered the most sustainable energy production method without causing pollutions to the environment. In overall, this review briefly summarizes thermolytic, electrolytic, photolytic and biolytic water splitting. It highlights photonic and electrical driven water splitting together with photovoltaic‐integrated solar‐driven water electrolysis.

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In this study, an advanced Kinect sensor was adopted to acquire infrared radiation (IR) images for liveness detection. The proposed liveness detection method based on infrared radiation (IR) images can deal with face spoofs. Face pictures were acquired by a Kinect camera and converted into IR images. Feature extraction and classification were carried out by a deep neural network to distinguish between real individuals and face spoofs. IR images collected by the Kinect camera have depth information. Therefore, the IR pixels from live images have an evident hierarchical structure, while those from photos or videos have no evident hierarchical feature. Accordingly, two types of IR images were learned through the deep network to realize the identification of whether images were from live individuals. In comparison with other liveness detection cross-databases, our recognition accuracy was 99.8% and better than other algorithms. FaceNet is a face recognition model, and it is robust to occlusion, blur, illumination, and steering. We combined the liveness detection and FaceNet model for identity authentication. For improving the application of the authentication approach, we proposed two improved ways to run the FaceNet model. Experimental results showed that the combination of the proposed liveness detection and improved face recognition had a good recognition effect and can be used for identity authentication.

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