The methods for dealing with influence of light and complex background often consume large calculation and long time. To solve this problem, a general method of gesture recognition in complex environment was proposed. The proposed method was based on the binary Support Vector Machine (SVM) and bitwise operation instead of sliding window to achieve the goal of rapid screening, and then Compute Unified Device Architecture (CUDA) was used to build a convolutional neural network to re-judge the initial screen area. The proposed method does not rely on dynamic gesture recognition techniques, and can be used for both dynamic and static gesture recognition. The method can deal with the problem of illumination change and background interference. The experimental results show that compared with the methods based on sliding window, the computational efficiency is improved by 100 to 1000 times. It takes less than 0.01 s to process a picture. The experimental results on the modified Marcel data set show that its precision achieves 96.1% and recall achieves 100%. The proposed algorithm can be used for real-time hand gesture recognition under complex environment for its high performance.

According to the software network communication behavior, a reverse analytical method based on session association was proposed in this paper. The method restored the network traffic communication session and Application Programming Interface (API) sequence session produced by software firstly, then associated the sessions restored. Through the association, a direct mapping was built between two kinds of software network behavior analytical methods based on execution trace analysis and network traffic analysis respectively. The prototype system was designed and completed. Based on the system, the function call list was extracted. The reverse analytical method based on session association makes the reverse analysis of software network behaviors fast and convenient.