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This paper describes the recognition of multimodal biometric systems based on the face and palmprint features. The common feature extraction technique between the palmprint and face recognition system is the Local Binary Pattern. In the palmprint recognition, the region of palm was extracted form the entire hand. The Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) were used to extract the features of palm. The Zernike moments and LBP were used to extract the features of face. The features of palm and face were integrated as feature vector. To evaluated the accuracy of the system, different classifications were applied such as SVM, KNN, Linear Discriminant. This experiment was performed on the ORL and CASIA database. The proposed system was tested on the said database and found to be satisfactory.

Palm-print and iris biometric traits fusion are implemented in this paper. The region of interest (ROI) of a palm is extracted by using the valley detection algorithm and the ROI of an iris is extracted based on the neighbor-pixels value algorithm (NPVA). Statistical local binary pattern (SLBP) is applied to extract the local features of palm and iris. For enhancing the palm features, a combination of histogram of oriented gradient (HOG) and discrete cosine transform (DCT) is applied. Gabor-Zernike moment is used to extract the iris features. This experimentation was carried out in two modes: verification and identification. The Euclidean distance is used in the verification system. In the identification system, the fuzzy-based classifier was proposed along with built-in classification functions in MATLAB. CASIA datasets of palm and iris were used in this research work. The proposed system accuracy was found to be satisfactory.

This paper presents the multimodal of face and iris based on the same feature extraction techniques for the both traits. For locating the iris, compute the next three neighbour-pixels from the pupil-circle in horizontal-right and horizontal-left, and if it found the value of those pixels are close to the sclera values (because it is close to the white color), then it will stop otherwise continue to the next pixels. The radius of the iris is obtained from the number of skipped-pixels. The iris is normalized by using the Rubber Sheet Normalization. The texture features of iris and face were extracted by using the Local Binary Pattern (LBP) and Gabor-Zernike Moments. Different classifications were used to evaluate the proposed system. The experiment was performed on the ORL and CASIA-Iris databases and the performance of the system is found to be satisfactory.