Hand gesture recognition is an intuitive and effective way for humans to interact with a computer due to its high processing speed and recognition accuracy. This paper proposes a novel approach to identify hand gestures in complex scenes by the Single-Shot Multibox Detector (SSD) deep learning algorithm with 19 layers of a neural network. A benchmark database with gestures is used, and general hand gestures in the complex scene are chosen as the processing objects. A real-time hand gesture recognition system based on the SSD algorithm is constructed and tested. The experimental results show that the algorithm quickly identifies humans’ hands and accurately distinguishes different types of gestures. Furthermore, the maximum accuracy is 99.2%, which is significantly important for human-computer interaction application.

This paper studies a novel scheme of watermarking relational databases for copyright protection based genetic algorithms. In this algorithm, GA is used in watermark signal processes, and associated novel watermark insertion algorithm and detection algorithm are proposed. Thus, the watermark signal in this method is expected to be more meaningful and has closer correlative to the copyright holder. The experimental results verify that the proposed algorithm is feasible, effective and robust.

By introducing public key cryptography, a novel algorithm for watermarking relational databases is proposed. In this algorithm, asymmetric keys are used in insetting and detecting database watermark processes, and users can't get anything of the private key copyright watermark information or destroy database watermark through public key. Watermark detection can be completed by the third party using public key without secret leaking, which can be applied to large-scale business. Experiment verifies the validity and security of database watermark algorithm in practice.

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