Biometrics is a term used to determine an individual's identification based on physiological or behavioral traits. Such physiological or behavioral characteristics differ from person to person. For this reason, it is more secure and popular to authenticate the person using biological characteristics than other conventional authentication methods. The Local Binary Pattern (LBP) face recognition system is widely used but is noise sensitive. For the purpose of improving the performance, a descriptor of a local texture called Local Ternary Pattern (LTP) is introduced, which is more discriminating in uniform regions and less noise sensitive. The proposed method called Enhanced LTP (ELTP), uses pre-processing technique. Here, the input image is pre-processed using Gamma Correction and Histogram Equalization. The LTP is applied on pre-processed image to get the finalized feature vectors. Experimentation is conducted on the standard datasets ORL, UMIST and VTU (VISA) face datasets. It is proved that ELTP shows better accuracy than other face recognition methods.
Face recognition is one of the applications in image processing that recognizes or checks an individual's identity. 2D images are used to identify the face, but the problem is that this kind of image is very sensitive to changes in lighting and various angles of view. The images captured by 3D camera and stereo camera can also be used for recognition, but fairly long processing times is needed. RGB-D images that Kinect produces are used as a new alternative approach to 3D images. Such cameras cost less and can be used in any situation and any environment. This paper shows the face recognition algorithms’ performance using RGB-D images. These algorithms calculate the descriptor which uses RGB and Depth map faces based on local binary pattern. Those images are also tested for the fusion of LBP and DCT methods. The fusion of LBP and DCT approach produces a recognition rate of 97.5% during the experiment.
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