Data Mining and Face Recognition

Data mining is definitely an emerging multidisciplinary subject that facilitates discovering of previously unknown correlations, patterns and trends from big amounts of data located in multiple data sources. It is a powerful new technology with good potential to help businesses produce full use of the accessible data for competitive advantages. Data mining application achievement reports have been informed in various areas among them, healthcare, Banking and finance, telecommunication and artificial intelligence. Classification is the most commonly applied data mining technique which is used in face recognition systems. A face recognition system is a dynamic topic in the field of biometrics. The human face has a principal role, which consists of complicated combination of features that allow us to communicate emotions and express our feelings. Principal Components Analysis (PCA) and Kernel Principal Components Analysis (KPCA) are techniques that have been used in face feature extraction and recognition. In this paper, data mining technique, key challenges in data mining, application of data mining is evaluated and kernel PCA algorithm is use for face recognition. To find Recall of KPCA algorithm Yela database is used to shows Kernel-PCA performance.