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.

Wireless Mesh Network (WMN) is a multi-hop, multi-path network that has become the most favored method in delivering end-to-end data, voice and video. Data transmission through WMN has the security and reliability, same as the conventional wired networks. Since, WMN has a decentralized topology, maintaining QoS is very crucial. Hence in this work, we propose to develop a WMN that selects services based on high QoS. In order to avoid redundancy in data transmission, in this work we propose to develop an efficient framework for multicasting by determining the most effective path for transmitting the same data towards multiple destination nodes. By simulation results, we show that the proposed technique provides better QoS in terms of throughput and packet delivery ratio.

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