Person Authentication Using Face Detection

Abstract

This paper presents a technique for Person authentication using face detection. A skin color based technique is used for detecting frontal human face from the input image. The visual features namely Profile Fourier Coefficients (PFC) are then extracted using template matching. Using Principal Component Analysis (PCA), the extracted features are projected into one-dimensional subspace. This approach preserves the Euclidean structure of face subspace that contains the local information. The projected face image is recognized using K nearest neighbor (KNN) classifier. This method is better and efficient than other existing methods due to its reduced error rate. It works for images taken under various lighting conditions and is also independent of the image type and resolution. The proposed Detection and Recognition algorithm (DR) can detect multiple faces of different sizes with a wide variety of facial variations. Further, the algorithm can detect both dark skin-tone and bright skin-tone because of the nonlinear transform of the Cb-Cr color space.