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Deep learning has achieved a great success in face recognition (FR), however, little work has been done to apply deep learning for face photo-sketch recognition. This paper proposes an adaptive scale local binary pattern extraction method for optical face features. The extracted features are classified by Gaussian process. The most authoritative optical face test set LFW is used to train the trained model. Test, the test accuracy is 98.7%. Finally, the face features extracted by this method and the face features extracted from the convolutional neural network method are adapted to sketch faces through transfer learning, and the results of the adaptation are compared and analyzed. Finally, the paper tested the open-source sketch face data set CUHK Face Sketch database(CUFS) using the multimedia experiment of the Chinese University of Hong Kong. The test result was 97.4%. The result was compared with the test results of traditional sketch face recognition methods. It was found that the method recognized High efficiency, it is worth promoting.

With the development of mobile internet, real-time target detection using mobile devices has wide application prospects, but the computing power of the terminal greatly limits the speed and accuracy of target detection. Edge-cloud collaborative computing is the main method to solve the lack of computing power of mobile terminals. The current method can't settle the problem of computation scheduling in the edge-cloud collaboration system. Given the existing problems, this paper proposes the pruning technology of classical target detection deep learning networks; training and prediction offloading strategy of edge-to-cloud deep learning network; dynamic load balancing migration strategy based on CPU, memory, bandwidth, and disk state-changing in cluster. After testing, the edge-to-cloud deep learning method can reduce the inference delay by 50% and increase the system throughput by 40%. The maximum waiting time for operation can be reduced by about 20%. The efficiency and accuracy of target detection are effectively improved.

Recognizing pedestrian attributes has recently obtained increasing attention due to its great potential in person re-identification, recommendation system, and other applications. Existing methods have achieved good results, but these methods do not fully utilize region information and the correlation between attributes. This paper aims at proposing a robust pedestrian attribute recognition framework. Specifically, we first propose an end-to-end framework for attribute recognition. Secondly, spatial and semantic self-attention mechanism is used for key points localization and bounding boxes generation. Finally, a hierarchical recognition strategy is proposed, the whole region is used for the global attribute recognition, and the relevant regions are used for the local attribute recognition. Experimental results on two pedestrian attribute datasets PETA and RAP show that the mean recognition accuracy reaches 84.63% and 82.70%. The heatmap analysis shows that our method can effectively improve the spatial and the semantic correlation between attributes. Compared with existing methods, it can achieve better recognition effect.

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