Multi-feature face recognition based on PSO-SVM

Face recognition is a kind of identification and authentication, which mainly use the global-face feature. Nevertheless, the recognition accuracy rate is still not high enough. This research aims to develop a method to increase the efficiency of recognition using global-face feature and local-face feature with 4 parts: the left-eye, right-eye, nose and mouth. We used 115 face images from BioID face dataset for learning and testing. Each-individual person's images are divided into 3 different images for training and 2 different images for testing. The processed histogram based (PHB), principal component analysis (PCA) and two-dimension principal component analysis (2D-PCA) techniques are used for feature extraction. In the recognition process, we used the support vector machine (SVM) for classification combined with particle swarm optimization (PSO) to select the parameters G and C automatically (PSO-SVM). The results show that the proposed method could increase the recognition accuracy rate. © 2012 IEEE

Multi-feature face recognition based on 2D-PCA and SVM

© 2013 Springer Science+Business Media, LLC. All rights reserved. Identification and authentication by face recognition mainly use global face features. However, the recognition accuracy rate is still not high enough. This research aims to develop a method to increase the efficiency of recognition using global-face feature and local-face feature with four parts: the left-eye, right-eye, nose and mouth. This method is based on geometrical techniques used to find location of eyes, nose and mouth from the frontal face image. We used 115 face images for learning and testing. Each-individual person's images are divided into three difference images for training and two difference images for testing. The Two-Dimension Principle Component Analysis (2D-PCA) technique is used for feature extraction and the Support Vector Machine (SVM) method is used for face recognition. The results show that the recognition percentage is 97.83%

Analyzing Motion Parameters Using Unsupervised Fuzzy C-Prototypes

Motion-based segmentation plays an important role in dynamic scene analysis of video sequence. This technique should not only cluster the feature vectors but also extract the optimum number of clusters that correspond to the moving objects. The motion features of moving objects in a video sequence have to be extracted so that segmentation can be performed based on this information. In this paper, we present a scheme for extracting moving objects. First, the dense optical flow fields are calculated to extract motion vectors. Surface fitting is performed over the parametric motion model. Then, an unsupervised robust fuzzy C-Prototypes clustering technique is applied to motion-based segmentation in the parameter space. Finally, the individual moving object and background can be represented in layers. Experimental results showing the significance of ths proposed method are provided. 1 Introduction Recent technology in digital video processing has moved to "content-based" storage and retr..

Informing Science InSITE - "Where Parallels Intersect" June 2003 Paper Accepted as an Informal Paper

Computer-assisted Instruction (CAI) plays very important role in e-learning system. Distance- learning students can remotely access this kind of course material. As CAI materials are in electronic form they are subject to illegal manipulation and duplication. Therefore, there is growing need to develop robust techniques for protecting these materials. Digital watermarking has been proposed as a solution to the problem of copyright protection of multimedia for many decades. This technique can also be applied to the educational materials. In this paper, a protection scheme is proposed in which double watermarks are embedded into all still images in the CAI materials before they are distributed. Firstly, the visible watermark, e.g., university's logo, is inserted directly on image pixel's intensity to exhibit an ownership. An invisible, semi- fragile watermark is then also embedded on these watermarked images. Because of the special characteristic of the latter watermark, any attempt to change or remove the visible logo can be clearly detected. We have also developed an extraction method to reveal the secret watermarks which verify our right on the CAI materials. We have conducted experiments using different kinds of attacks on the watermarked images. The results of these experiments are discussed and conclusions presented as to the reliability and applicability of our proposed scheme