Fast face recognition based on Wavelet Transform on PCA

Today the word is moving towards the globalization in area of biometrics as an individual identification method. The techniques which are established for an identifying the individual using face as a biometric has become more importance in field of biometrics. The face database extracted leads the many application like photography, security surveillance, database identification etc. This paper includes the study of facial feature extraction techniques that are Principal Component Analysis (PCA) and Discrete Wavelet Transforms (DWT), hear the comparison of two given algorithms have been made with concerned to the rate of feature extraction for face recognition using the Principal Component Analysis (PCA) and the PCA using Discrete Wavelet Transforms (DWT). The proposed algorithm uses the concept of DWT for the image compression and PCA for the feature extraction and identification method. The limitations of the only PCA algorithm are a poor recognition speed and complex mathematical calculating load. To eliminate these limitations we are applying the DWT with different decomposition levels, i.e from level 0 to level 3 to facial image by using Daubechies Transform and applying the PCA for feature extraction process. The Euclidean Distance Measures system is used to find the nearest matching features in the whole database. In this paper the the mentioned algorithms are compared with their feature extraction and recognition time, the second parameter is the percentage of recognition of a test image. The results shows that the PCA with DWT applied gives higher recognition rate up to 93% than only PCA, with very less access time.