Gaussian Mixture Model

Gaussian Mixture Model– based feature extraction technique follow by accepted subspace methods for accurate multimodal biometric system. There are four dissimilar feature extraction techniques that are PCA Mixture Model, ICA I MM (Independent Component Analysis I Mixture Model), SVD MM (Singular Value Decomposition Mixture Model), and ICA II MM (Independent Component Analysis II Mixture Model) to intend a multimodal biometric system at feature level. The designed methods start in on with modeling the multimodal biometrics data with Gaussian Mixture Model followed by a subspace method like SVD, ICAII, ICAI and PCA. Broad-spectrum experiments are carried out to observe the authentication show of the proposed method at characteristic and match

Database - The ORL database contains 400 facial images: ten images of one user. The size of each image is 92*112 with 256 gray levels. Fig. 2 shows ten images of two users. Two feature extraction methods are in employment, first one is base on the SP (statistics properties) of the biometric images and the other is the traditional 2D principal component analysis (2DPCA). The minimum distance rule (MDR) is adopt for fusion of the match score level and compare the outcome of the multimodality recognition with the results of the unimodal palmprint and face recognition. The results shows the performance of multimodality outperforms the unimodal recognition and the correctness can reach 100% based on ORL and PolyU database using the fusion rule at the match score level

The assortment of significantly sparse 2D KCM-base facial appearance according to the planned approach results in highly inventive face identification method as compare to the other methods with the purpose of use orthogonal moments such as the 2DZernike, 2DTchebiche for 2DGaussian-Hermite. Experiment on demanding databases (viz., CK-AUC and FRGC) and comparison with the good established projection, texture, and instant based methods specify superior identification performance in terms of mean rightness and robustness of the future holistic-or hybrid-type discriminative KCM-base method, particularly when sample sizes are little and the intra collection faces have significant variations due to expressions