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Face quality evaluation can filter out low quality face image to save computational resources and improve the system performance, labeling the face image quality score by manual consume too much manpower. To solve this problem, an unsupervised face image evaluation based on face recognition is proposed. We use the face recognition model to calculate the features of faces and label the images quality score. The face recognition model is compressed by knowledge distillation method to obtain efficient quality assessment model. Experimental results show that this method can effectively evaluate the quality of face image and improve the performance of face recognition.

The safe and reliable operation of power transmission lines is the key to the sustainable and stable operation of power grids. Using the artificial intelligence techniques for the channel status monitoring greatly improves the inspection efficiency of transmission lines. However, the discrimination is mostly conducted on the cloud side, which does not meet the requirement of real-time processing. In this paper, we build an intelligent monitoring system for transmission lines based on edge computing. According to the specific characteristics of the real-world power filed applications, a lightweight deep learning algorithm model is designed. It is then transplanted on a self-developed AI chip and a field test is carried out. The experimental results show that the model designed in this paper balances well between the accuracy, the model size and the inference speed. It realizes status local discrimination of the transmission lines, and promising detection results are obtained.

This paper presents a novel CNN model called comparison prediction network for apparent age estimation. The algorithm is structured by feature extraction and Face feature database. Compared with the existing methods, our algorithm can better deal with the problem caused by differences between apparent age and actual age, which improves the prediction precision of the model with the increasing credibility and robustness of the model prediction results, enhancing the generalization ability of the model. The algorithm has fewer parameters and is lighter than other methods, which is suitable for mobile deployment.

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