Abstract
Biometric systems (BS) helps in reorganization of individual person based on the biological traits like ears, veins, signatures, voices, typing styles, gaits, etc. As, the Uni-modal BS does not give better security and recognition accuracy, the multimodal BS is introduced. In this paper, biological characters like face, finger print and iris are used in the feature level fusion based multimodal BS to overcome those issues. The feature extraction is performed by Bi-directional Empirical Mode Decomposition (BEMD) and Grey Level Co-occurrence Matrix (GLCM) algorithm. Hilbert-Huang transform (HHT) is applied after feature extraction to obtain local features such as local amplitude and phase. The combination of BEMD, HHT and GLCM are used for achieving effective accuracy in the clas-sification process. MMB-BEMD-HHT method is used in Multi-class support vector machine technique (MC-SVM) as a classifier. The false rejection ratio has improved using feature level fusion (FLF) and MC-SVM technique. The performance of MMB-BEMD-HHT method is measured based on the parameters like False Acceptance Ratio (FAR), False Rejection Ratio (FRR), and accuracy and compared it with an existing system. The MMB-BEMD-HHT method gave 96% of accuracy for identifying the biometric traits of individual persons.
Highlights
MMBS uses two or more physiological or behavioural characteristics for identification and it is more convenient than traditional authentication techniques
This MMB-Bi-directional Empirical Mode Decomposition (BEMD)-Hilbert-Huang transform (HHT) method was developed with the biometric features of face, iris and fingerprint to enhance the security of the desired system
Total 150 images were used in MMB-BEMD-HHT training and this 150 images comprised of 50 face images, 50 www.ijacsa.thesai.org
Summary
MMBS uses two or more physiological or behavioural characteristics for identification and it is more convenient than traditional authentication techniques. Feature level fusion (FLF) helps to extract the features from fingerprint and face. Score level fusion occurs over the finger vein, fingerprint, finger shape and finger knuckle print features from the human and it performs based on the triangular norm. Match score level fusion combines the features of face and signature. The major contributions of this research work is stated as follows: 1) To improve the accuracy of the image recognition, two different feature extraction techniques are combined here such as HHT and GLCM. 2) The FLF is used for fusing the feature vectors of finger print, face and iris This FLF is pre classification technique which is used before matching and it gives more accurate results when compared to post classification techniques such as score level and decision level fusion. The conclusion of this research work is given at the end
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