With the rapid growth in multimedia contents, among such content face recognition has got much attention especially in past few years. Face as an object consists of distinct features for detection; therefore, it remains most challenging research area for scholars in the field of computer vision and image processing. In this survey paper, we have tried to address most endeavoring face features such as pose invariance, aging, illuminations and partial occlusion. They are considered to be indispensable factors in face recognition system when realized over facial images. This paper also studies state of the art face detection techniques, approaches, viz. Eigen face, Artificial Neural Networks (ANN), Support Vector Machines (SVM), Principal Component Analysis (PCA), Independent Component Analysis (ICA), Gabor Wavelets, Elastic Bunch Graph Matching, 3D morphable Model and Hidden Markov Models. In addition to the aforementioned works, we have mentioned different testing face databases which include AT & T (ORL), AR, FERET, LFW, YTF, and Yale, respectively for results analysis. However, aim of this research is to provide comprehensive literature review over face recognition along with its applications. And after in depth discussion, some of the major findings are given in conclusion.

This document may differ from the final, published version of the research and has been made available online in accordance with publisher policies. To read and/or cite from the published version of the research, please visit the publisher's website (a subscription may be required.)Preventing identity theft: identifying major barriers to knowledge-sharing in online retail organisations Abstract Purpose -Knowledge-sharing (KS) for preventing identity theft has become a major challenge for organisations. The purpose of this paper is to fill a gap in the literature by investigating barriers to effective KS in preventing identity theft in online retail organisations. Design/methodology/approach -A framework was proposed based on a reconceptualisation and extension of the KS enablers framework (Chong et al., 2011). A qualitative case study research method was used for the data collection. Thirty-four semi-structured interviews were conducted in three online retail organisations in the UK. Findings -The findings suggest that the major barriers to effective KS for preventing identify theft in online retail organisations are: lack of leadership support; lack of employee willingness to share knowledge; lack of employee awareness of KS; inadequate learning opportunities; lack of trust in colleagues; insufficient information-sourcing opportunities and information and communications technology (ICT) infrastructure; a weak KS culture; lack of feedback on performance; and lack of job rotation. Practical implications -The research provides solutions for removing existing barriers to KS in preventing identity theft. This is important to reduce the number of cases of identity theft in the UK. Originality/value -This research extends knowledge of KS in a new context: preventing identity theft in online retail organisations. The proposed framework extends the KS enablers framework by identifying major barriers to KS in the context of preventing identity theft.

In computer science field, one of the basic operation is sorting. Many sorting operations use intermediate steps. Sorting is the procedure of ordering list of elements in ascending or descending with the help of key value in specific order. Many sorting algorithms have been designed and are being used. This paper presents performance comparisons among the two sorting algorithms, one of them merge sort another one is quick sort and produces evaluation based on the performances relating to time and space complexity. Both algorithms are vital and are being focused for long period but the query is still, which of them to use and when. Therefore this research study carried out. Each algorithm resolves the problem of sorting of data with a unique method. This study offers a complete learning that how both of the algorithms perform operation and then distinguish them based on various constraints to come with outcome.

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