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 characte...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.展开更多
To overcome the high computational complexity in real-time classifier design, we propose a fast classification scheme. A new measure called 'reconstruction proportion' is exploited to reflect the discriminant ...To overcome the high computational complexity in real-time classifier design, we propose a fast classification scheme. A new measure called 'reconstruction proportion' is exploited to reflect the discriminant information. A novel space called the 'reconstruction space' is constructed according to the reconstruction proportions. A point in the reconstruction space denotes the case of a sample reconstructed using training samples. This is used to search for an optimal mapping from the conventional sample space to the reconstruction space. When the projection from the sample space to the reconstruction space is obtained, a new sample after mapping to the new discriminant space would be classified quickly according to the reconstruction proportions in the reconstruction space. This projection technique results in a diversion of time-consuming calculations from the classification stage to the training stage. Though training time is prolonged, it is advantageous in that classification problems such as identification can be solved in real time. Experimental results on the ORL, Yale, YaleB, and CMU PIE face databases showed that the proposed fast classification scheme greatly outperforms conventional classifiers in classification accuracy and efficiency.展开更多
文摘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.
文摘To overcome the high computational complexity in real-time classifier design, we propose a fast classification scheme. A new measure called 'reconstruction proportion' is exploited to reflect the discriminant information. A novel space called the 'reconstruction space' is constructed according to the reconstruction proportions. A point in the reconstruction space denotes the case of a sample reconstructed using training samples. This is used to search for an optimal mapping from the conventional sample space to the reconstruction space. When the projection from the sample space to the reconstruction space is obtained, a new sample after mapping to the new discriminant space would be classified quickly according to the reconstruction proportions in the reconstruction space. This projection technique results in a diversion of time-consuming calculations from the classification stage to the training stage. Though training time is prolonged, it is advantageous in that classification problems such as identification can be solved in real time. Experimental results on the ORL, Yale, YaleB, and CMU PIE face databases showed that the proposed fast classification scheme greatly outperforms conventional classifiers in classification accuracy and efficiency.
