Face anti‐spoofing is critical to prevent face recognition systems from a security breach. The biometrics community has achieved impressive progress recently due to the excellent performance of deep neural networks and the availability of large datasets. Although ethnic bias has been verified to severely affect the performance of face recognition systems, it still remains an open research problem in face anti‐spoofing. Recently, a multi‐ethnic face anti‐spoofing dataset, CASIA‐SURF cross‐ethnicity face anti‐spoofing (CeFA), has been released with the goal of measuring the ethnic bias. It is the largest up to date CeFA dataset covering three ethnicities, three modalities, 1607 subjects, 2D plus 3D attack types and the first dataset including explicit ethnic labels among the recently released datasets for face anti‐spoofing. We organized the Chalearn Face Anti‐spoofing Attack Detection Challenge which consists of single‐modal (e.g. RGB) and multi‐modal (e.g. RGB, Depth, infrared) tracks around this novel resource to boost research aiming to alleviate the ethnic bias. Both tracks have attracted 340 teams in the development stage, and finally, 11 and eight teams have submitted their codes in the single‐modal and multi‐modal face anti‐spoofing recognition challenges, respectively. All of the results were verified and re‐ran by the organizing team, and the results were used for the final ranking. This study presents an overview of the challenge, including its design, evaluation protocol and a summary of results. We analyse the top‐ranked solutions and draw conclusions derived from the competition. Besides, we outline future work directions.

Wind power generation is one of the renewable energy generation methods which maintains good momentum of development at present. However, its extremely intense intermittences and uncertainties bring great challenges to wind power integration and the stable operation of wind power grids. To achieve accurate prediction of wind power generation in China, a hybrid prediction model based on the combination of Wavelet Decomposition (WD) and Long Short-Term Memory neural network (LSTM) is constructed. Firstly, the nonstationary time series is decomposed into multidimensional components by WD, which can effectively reduce the volatility of the original time series and make them more stable and predictable. Then, the components of the original time series after WD are used as input variables of LSTM to predict the national wind power generation. Forty points were used, 80% as training samples and 20% as testing samples. The experimental results show that the MAPE of WD-LSTM is 5.831, performing better than other models in predicting wind power generation in China. In addition, the WD-LSTM model was used to predict the wind power generation in China under different development trends in the next two years.

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NO2 pollution has caused serious impact on people's production and life, and the management task is very difficult. Accurate prediction of NO2 concentration is of great significance for air pollution management. In this paper, a NO2 concentration prediction model based on long short-term memory neural network (LSTM) is constructed with daily NO2 concentration in Beijing as the prediction target and atmospheric pollutants and meteorological factors as the input indicators. Firstly, the parameters and architecture of the model are adjusted to obtain the optimal prediction model. Secondly, three different sets of input indicators are built on the basis of the optimal prediction model to enter the model learning. Finally, the impact of different input indicators on the accuracy of the model is judged. The results show that the LSTM model has high application value in NO2 concentration prediction. The maximum temperature and O3 among the three input indicators improve the prediction accuracy while the NO2 historical low-frequency data reduce the prediction accuracy.

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