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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.

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Abstract-High feature vector dimension quietly remained a curse element for Content Based Image Retrieval (CBIR) system which eventually degrades its efficiency while indexing similar images from database. This paper proposes CBIR system using Gray Scale Weighted Average technique for reducing the feature vector dimension. The proposed method is more suitable for color and texture image feature analysis as compared to color weighted average method as illustrated in literature review. To prove the effectiveness of retrieval system, two standard benchmark dataset namely, Wang and Amsterdam Library of Texture Images (A LOT) for color and texture have been selected to evaluate the system retrieval accuracies as well as efficiencies generated by each method. For the purpose of image similarity, Euclidean distance has been employed which matches query image feature vector with image database feature vectors. The experimental results generated by two methods showed that overall performance of the proposed method is relatively better in terms of average precision, average recall and its average retrieval time.