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Recently, three Dimension (3D) multimedia technology has grown over a wide range, where the 3D used in many applications such as medical, television, cinema, game, education, etc. The developments also include the computer network, these evaluations make the store and distribute the media very easy and at the same time very critical, where any person can access these data and manipulate it or redistribute it with illegal copyrights. Therefore, it is necessary to find a watermarking technique to keep the security of these media. 3D multimedia, especially 3D Video contains a huge amount of data, trying achievement copyright protection for all these data consumes the computation, and slow down securing process, thus 3D video key frame extraction process is necessary. In this paper, the histogram-based key frame extraction technique is applied to extract essential and important data of the 3D video. A modified invisible, and blind watermark system is suggested in this research for 3D Video copyrights and authentication protection using Dual-Tree Complex Wavelet Transform (DTCWT). The experiment results show that 3D video key extraction system has given high compression ratio ( from 4:1 to 5:1) and the protection system could put an invisible watermark while keeping the quality of the 3D video, as well as the proposed watermark system, is robust where the watermark extracted successfully after applying a different types of attack.

For fast and efficient automatic identity verification, biometric technology has developed rapidly. As one of the useful biometric identification technologies is face recognition. The applications of the face identification system-wide such as criminal detection, security verification, credit card verification, medicine, video conference, and other occasions. The main elements of the pattern recognition system include preprocessing, feature extraction, and classification. The feature extraction stage is concerned in this paper in order to suggest a robust algorithm useful in the classification stage. Many types of research try to recognize the face utilizing various algorithms, comparison among these algorithms is presented in this paper to show the effect of the feature extraction algorithm on human face classification. The main goal of this research is to combine Gray Level Co-Occurrence Matrix (GLCM) and Center-Symmetric Local Binary Pattern (CS-LBP) in a manner way to be used for extracting the texture feature of the human face classification. The experimental result explains that the proposed method given high accuracy in recognizing the human face where the proposed algorithm capable of recognizing 28 humans under different poses and illumination conditions.