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

Biometrics; real-time multimodal biometrics; real-time face recognition; feature analysis