Real-Time Multimodal Biometric Authentication of Human Using Face Feature Analysis

As multimedia data sharing increases, security in mobile devices and its mechanism can be seen as critical. Biometrics combines the physiological behavioral qualities of an individual to validate their character real-time. Humans incorporate attributes like a fingerprint, face, iris, palm print, finger knuckle Deoxyribonucleic Acid (DNA), walk, voice, mark, or keystroke. The main goal this paper is design robust framework for automatic face recognition. Scale Invariant Feature Transform (SIFT) Speeded-up Robust Features (SURF) are employed Also, we propose modified Gabor Wavelet SIFT/SURF (GWT-SIFT/GWT-SURF) increase recognition accuracy human faces. proposed scheme composed three steps. First, entropy image removed using Discrete (DWT). Second, computational complexity reduced. Third, increased authentication by GWT-SIFT/GWT-SURF algorithm. A comparative analysis done on real-time Olivetti Research Laboratory (ORL) Poznan University Technology (PUT) databases. When compared traditional methods, verify that GWT-SIFT achieves better 99.32% approach GWT-SURF run time 100 images 3.4 seconds when which has 4.9 images.