The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Unknown awareness is very important for many applications such as face recognition. In a typical unknown aware classifier, an “unknown” label is assigned to strange test instances. This study proposes an unknown aware classifier known as UAkNN by extending the well-known kNN classifier. In UAkNN, unknown awareness is achieved by exploiting distances between instances of individual classes. These distances...
This study elaborates on a design of a face recognition algorithm realized with feature extraction from 2D-LDA and the use of polynomial-based radial basis function neural networks (P-RBF NNS). The overall face recognition system consists of two modules such as the preprocessing part and recognition part. The proposed polynomial-based radial basis function neural networks is used as an the recognition...
Matching near-infrared (NIR) face images to visible light (VIS) face images offers a robust approach to face recognition with unconstrained illumination. In this paper we propose a novel method of heterogeneous face recognition that uses a common feature-based representation for both NIR images as well as VIS images. Linear discriminant analysis is performed on a collection of random subspaces to...
Here an efficient fusion technique for automatic face recognition has been presented. Fusion of visual and thermal images has been done to take the advantages of thermal images as well as visual images. By employing fusion a new image can be obtained, which provides the most detailed, reliable, and discriminating information. In this method fused images are generated using visual and thermal face...
The techniques of eigenfaces and neural net-based algorithms (LS-SVM and BP NNs) are combined to categorize gender from facial images in this paper. Based on exploration of the related techniques, the eigenfaces were firstly established from the training images, and the projection coefficients for training and testing images obtained in the space spanned by the eigenfaces; after that the LS-SVM and...
A community intrusion detection system based on wavelet neural network (WNN) is presented in this paper. This system is composed of ARM (Advanced RISC Machines) data acquisition nodes, wireless mesh network and control centre. The data acquisition node uses sensors to collect information and processes them by image detection algorithm, and then transmits information to control centre with wireless...
Face recognition technology has been an increasingly important module in security systems. A challenging problem is how to extract features tolerant to the appearance variables such as changes in shape, illumination, and occlusion. Extracted metrical features of facial caricatures that are combined with their facial photographs in the training set are examined. The facial caricature is a personal...
For its sensitive dependence with the initial value, chaos can be used to the pattern recognition of the ones with extremely small difference. An algorithm based on chaotic fuzzy RBF neural network (CFRBF) is proposed and used for face recognition. For introducing chaotic noise, the network obtains a better anti-jamming. It can avoid being affected by the factors such as illumination and gesture....
This paper presents a developed face recognition system. The method used singular valued decomposition as images feature extractor and back propagation neural networks as its classifier. The back propagation training parameters are varied in order to find the best parameter with the highest performance accuracy. The results from experiments have showed that combinations of both methodologies give...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.