Hybrid Biometric System Using Iris and Speaker Recognition

In this study, a hybrid security system is proposed. The proposed system is composed of two subsystems namely iris recognition system (IRS) and speaker recognition system (SRS). Pre-processing, feature extraction and feature matching are the main steps of these systems. In IRS subsystem, Gaussian filter, Canny edge detector, Hough transform, and histogram equalization is performed for pre-processing, respectively. After that, by applying 4-level Discrete Wavelet Transform (DWT) to pure iris image, the iris image is decomposed into four sub-bands (LL4, LH4, HL4 and HH4). In order to extract the feature vector from iris pattern, the LH4, HL4 and HH4 sub-bands (matrices) are merged into one matrix. Finally the matrix is transformed in vector to obtain the feature vector of iris image. For SRS subsystem, the pre-processing step includes spectral arrangement, silence part removing and band limitation operations. After pre-processing, frame blocking and windowing are applied to the long-term speech samples and then Fast Fourier Transform (FFT) is performed for the each short-term speech segments (frames). Finally, the Mel Frequency Cepstral Coefficients (MFCC) technique is performed in order to obtain feature vector of the speech. The feature matching step of both IRS and SRS is implemented with Dynamic Time Warping (DTW) which is an efficient algorithm to measure the distance between two vectors. According to the DTW results, the false acceptance rate (FAR) is zero and false rejecting rate (FRR) is about 4 % for the proposed hybrid system.