Due to numerous difficulties, including the variation in face shapes between individuals, the challenge of recognizing dynamic facial attributes, the poor quality of digital images, etc., detecting human emotion depending on facial expression is difficult for the computer vision community. Thus, in this study, we propose an approach for emotion recognition depending on facial expression using histogram of oriented gradients and convolution neural network (HOG-CNN). The HOG-CNN composed of three stages, median filter, HOG, and CNN. The first stage is preprocessing using median filter. The second stage is feature extraction using HOG. The third stage is classification using CNN. The proposed method was tested and evaluated on the UMD face database. The system attained a high performance with a mean average accuracy of 98.07%, average precision of 94.78%, and average recall of 97.15%.
Image Encryption is important for protecting image information, In this paper A chaos based on RC4 algorithm has been proposed to encrypt color Images, It is chaotic Henon function have created three keys , depending on the initial conditions to generate numbers dynamic function of the chaotic conditions in addition to the user's desire three dimensions of which mechanically operated from within the initial condition , and then working process distortion of the bits of the three keys Using the RC4 algorithm and results new in the process of XOR them to generate a unique key of binary bits one and zero and then turn it into a digital fracture and after the intervention to Phase image to encrypted so they generate the keys again, and the size of the desired image In order to encrypt it. the performance of the algorithm has been analyzed and results show that the algorithm has a very long key space, and high sensitivity for small changes in key which makes the algorithm Immune to Brute force attacks, and it can resist the differential and statistical attacks, in addition to having very high encryption and decryption speed, the receiver can detect any changes to the encrypted image during transmission. the algorithm has been implemented and analysis done by using Matlab R2008a software.
In order to reach the level of security between the two parties or a group of parties in the communication channel, the inputs of the protocol algorithm must be characterized by the sensitivity of the initial parameters and conditions involved in the chaotic functions with great ability to change in any very slight manipulation of the inputs by the attacker or intruder because there will be Much of the fundamental change in the values of the shared keys of the two parties or participants in the system, as well as the prime numbers and primitive roots, is hidden rather than public. Use the magic square system algorithm to find the magic constant from the values of the Defy protocol algorithm with the chaotic values of three dimensions, and this magic constant will give us the long values of the shared keys between the parties participating in the group or the server used to distribute the shared keys to the parties, ensuring that this protocol is not attacked by a third party trying to enter the communication channel. The performance of the algorithm was analyzed by conducting and measuring the efficiency of protocol implementation, analyzing key length and sensitivity, and finding the speed of algorithm performance within the acceptable range of the number of participants in the system. A protocol algorithm in Matlab R2013b was used to implement the algorithm and perform the analyses.
Given the importance of augmented reality in recent years and its wide spread in many fields of media industries, practical fields such as medicine, education, and others. This study present a methodology in order to insert virtual static object into a real video environment for improve the reality with a virtual information that was not actually present. There are several problems needs to be overcome during the process of merging the virtual object such as geometry of shapes, registration, and illumination. The aim of this study is to make the virtual static objects look like it actually exists in the same real video environment in terms of dimensions, sizes and consistency of shapes. The following steps of the proposed method are: Firstly convert original video file into frames. Secondly separate the background from foreground by rebuilding the background model, then obtain the foreground objects by subtract original frames of the video from background. Thirdly apply the background segmentation. Then extracting the features for every one of the segments and extracting the features of virtual static object. Fifthly selecting a suitable segment (area) for insert the virtual object.
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