Automated face recognition has become a major field of interest. Face recognition algorithms are used in a wide range of applications viz., security control, crime investigation, and entrance control in buildings, access control at automatic teller machines, passport verification, identifying the faces in a given databases. This paper discusses different face recognition techniques by considering different test samples. The experimentation involved the use of Eigen faces and PCA (Principal Component Analysis). Another method based on Cross-Correlation in spectral domain has also been implemented and tested. Recognition rate of 90% was achieved for the above mentioned face recognition techniques.
Clustering analysis is the problem of partitioning a set of objects O = {o1… on} into c self-similar subsets based on available data. In general, clustering of unlabeled data poses three major problems: 1) assessing cluster tendency, i.e., how many clusters to seek? 2) Partitioning the data into c meaningful groups, and 3) validating the c clusters that are discovered. We address the first problem, i.e., determining the number of clusters c prior to clustering. Many clustering algorithms require number of clusters as an input parameter, so the quality of the clusters mainly depends on this value. Most methods are post clustering measures of cluster validity i.e., they attempt to choose the best partition from a set of alternative partitions.In contrast, tendency assessment attempts to estimate c before clustering occurs. Here, we represent the structure of the unlabeled data sets as a Reordered Dissimilarity Image (RDI), where pair wise dissimilarity information about a data set including ‗n' objects is represented as nxn image. RDI is generated using VAT (Visual Assessment of Cluster tendency), RDI highlights potential clusters as a set of -dark blocks‖ along the diagonal of the image. So, number of clusters can be easily estimated using the number of dark blocks across the diagonal. We develop a new method called -Extended Dark Block Extraction (EDBE) for counting the number of clusters formed along the diagonal of the RDI. EDBE method combines several image and signal processing techniques.
Spam refers to unsolicited, unwanted and inappropriate bulk email. Spam filtering has become conspicuous as they consume a lot of network bandwidth, overloads the email server and drops the productivity of global economy. Content based spam filtering is accomplished with the help of multiple pattern string matching algorithm. Traditionally Aho Corasick algorithm was used to filter spam which constructs a trie of the spam keywords. The performance degrades in the context of time as well as space as the size of trie increases with the growing spam keywords count. To counterbalance time and space loss, bit parallel multiple pattern string matching algorithm using Shift OR method is used. The method acts as filter performing approximate string matching . This implies that there are some false matches detected by the filter which requires verification. The proposed method for filtering spams has been developed using a combination of Shift AND and OR operation. The method directly works on spam keywords of equal size whereas for unequal size keywords, a new proposed equal size grouping method is developed. Both method shows improvement over the Aho Corasick algorithm in context of space complexity and also behaves as an efficient filter and reducing the number of false matches as present in Shift OR method.
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