A new face recognition algorithm is proposed which is robust to variations in pose, expression and illumination. The framework is similar to the ubiquitous block matching algorithm used for motion estimation in video compression but has been adapted to compensate for illumination differences. One of the key differentiators of this approach is that unlike traditional face recognition algorithms, the image data representing the face or features extracted from the facial data is not used for classification. Instead, the mapping between the probe and gallery images given by the block matching algorithm is used to classify the faces for recognition. Once the mappings are found for each gallery image, the degree of bijectivity that each mapping produces is used to derive the similarity scores for recognition.

Manipulating big data distributed over a cluster is one of the big challenges which most of the current big data oriented companies face. This is evident by the popularity of MapReduce and Hadoop, and most recently Apache Spark, a fast, in-memory distributed collections framework which caters to provide solution for big data management. This paper, present a discussion on how technically Apache Spark help us in Big Data Analysis and Management.

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During data analysis, often data needs to be grouped together based on similar looking or behaving. As the real world data features modulate with the Big data, where the data is unlabeled, the task of dividing the population or data points into a number of groups with similar points is of prime necessity. This method of identifying similar groups of data in a data set is called clustering. In simple words, the aim is to segregate groups with similar traits and assign them into clusters. This paper presents the importance of the K-means Clustering algorithm to understand the inner structure of the data to obtain the areas wherein based on the number of car rides booked in an area, optimum pickup point can be found using K-Means Clustering Algorithm. General TermsBigdata

Wouldn't we love to replace passwords access control to avoid theft, forgotten passwords? Wouldn't we like to enter the security areas just in seconds? Yes the answer is face recognition. In this study we explore and compare the performance of three algorithms namely LDA, CCA, AAM. LDA (an evolution of PCA is a dimensionality reduction technique where it solves the problem of illumination to some extent, maximizing the inter class separation and minimizing the intra class variations. CCA, a measure of linear relationship between two multidimensional variables where it takes the advantage of PCA and LDA for maximizing the correlation and better performance. AAM is a model based approach where it just picks the landmarks of the images for recognition therefore reducing the error rate and producing good performance rate.