Facebook occupies an important part of people's time and life due to the recent developments in the field of communication technologies (CT). This paper aims at investigating the Facebook impact (FI) on undergraduate students at Charmo University. Moreover, Facebook also has a great impact on students' life studies at universities. However, in addition to its benefits, Facebook also has some side effects on students' life study. In regard to data collection, an online survey was used in which 15 questions were answered by 100 participants from different faculties at Charmo University. The data collection process was conducted from First of March to 30 of April 2016. The program of SPSS was used to analyze the results of the survey. The results of the current study show that students spend a significant amount of their times on using Facebook.
Over the last twenty years face recognition has made immense progress based on statistical learning or subspace discriminant analysis. This paper investigates a technique to reduce features necessary for face recognition based on local binary pattern, which is constructed by applying wavelet transform into local binary pattern. The approach is evaluated in two ways: wavelet transform applied to the LBP features and wavelet transform applied twice on the original image and LBP features. The resultant data are compared to the results obtained without applying wavelet transform, revealing that the reduction base one wavelet achieves the same or sometimes improved accuracy. The proposed algorithm is experimented on the Cambridge ORL Face database.
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