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Face recognition has always been a fascinating research area. It has drawn the attention of many researchers because of its various potential applications such as security systems, entertainment, criminal identification etc. Many supervised and unsupervised learning techniques have been reported so far. Principal component analysis (PCA) is a classical and(More)
This paper presents a memetic algorithm based new approach to feature selection in face recognition. In this work, principal component analysis (PCA) has been used for dimensionality reduction/feature extraction and memetic algorithms have been applied for selection of features in face recognition application. ORL face database has been used for performing(More)
— Unsupervised or Self-Organized learning algorithms have become very popular for discovery of significant patterns or features in the input data. The three prominent algorithms namely Principal Component Analysis (PCA), Self Organizing Maps (SOM), and Independent Component Analysis (ICA) have widely and successfully been used for face recognition. In this(More)
Independent Component Analysis (ICA) is one of the fastest growing fields in the area of neural networks and signal processing. Blind Source Separation (BSS) is one of the applications of ICA. In this paper, ICA has been used for separating unknown source signals. BSS is used to extract independent signal components from their observed linear mixtures at an(More)
In the future, high capacity downlink would be required in cellular system with quality of service provisioning. The quality of services (QoS) means acceptable data transfer rate, signal to noise ratio (SNR), and bit error rate (BER). It can be achieved by management of resources in orthogonal frequency division multiple access (OFDMA) system. The main(More)