Non-uniform face mesh for 3D face recognition

Uniform face meshes are able to represent the face in 3D format and can also be used to perform 3D face recognition.However, to obtain a good recognition rate, a fine mesh which consists of many points would be needed to accurately represent the many contours of the face.Therefore, in this paper, it is proposed that a non-uniform face mesh is constructed for 3D face recognition. A non-uniform mesh consisting of fine meshes for the middle of the face and coarse meshes for the rest of the face was created. In comparison with a uniform mesh, the proposed non-uniform face mesh consists of much fewer points and therefore saves storage space and transmission time due to a smaller file size.Besides that, the proposed mesh was able to produce recognition rates that were only slightly lower than the uniform mesh, hence proving that important face features for recognition were retained

Propagation measurement, drain, radio wave, electromagnetic, wireless communications.

Propagation measurement results were reported in a style of open-trench drain that is common in several Asian countries in [1]. In particular, measurement results at three frequencies, namely 900 MHz, 2.4 GHz, and 5.8 GHz were reported in three scenarios, whose findings are beneficial in designing wireless communication systems in environments where such drains are present. In this work, further investigation has been made on the contents inside an open-trench drain. This investigation is expected to contribute to a practically important propagation problem because in reality, the open-trench drains environments are not always dry and empty. On the contrary, they are sometimes filled with different contents, the most common one being water, as one of the primary objectives of a drain is to collect water runoff from the surrounding buildings and houses and convey it to an outfall. In other scenarios, the open-trench drain’s floor might be filled with liquids, soil, foliage from the surrounding trees, and even, trash

A new approach in solving illumination and facial expression problems for face recognition

In this paper, a novel dual optimal multiband features (DOMF) method is presented to increase the robustness of face recognition system to illumination and facial expression variations.The wavelet packet transform first decomposes image into low-, mid- and high-frequency subbands and the multiband feature fusion technique is incorporated to select the subbands that are invariant to illumination and expression variation separately.These subbands form the optimal feature sets.Parallel radial basis function neural networks are employed to classify these feature sets.The scores generated by the neural networks are combined by an adaptive fusion mechanism where the level of illumination variations of the testing image is estimated and the weights are assigned to the scores accordingly.The experimental results show that DOMF outperforms other algorithms and also achieves promising performance on illumination and facial expression variation conditions

Multi camera visual saliency using image stitching

This paper presents and investigates two models for a multi camera configuration with visual saliency capability. Applications in various imaging fields each have a different set of detection parameters and requirements which would result in the necessity of software changes. The visual saliency capability offered by this multi camera model allows generic detection of conspicuous objects be it human or nonhuman based on simple low level features. As multiple cameras are used, an image stitching technique is employed to allow combination of Field-of-View (FoV) from different camera captures to provide a panoramic detection field. The stitching technique is also used to complement the visual saliency model in this work. In the first model, image stitching is applied to individual captures to provide a wider FoV, whereby the visual saliency algorithm would able to operate on a wide area. For the second model, visual saliency is applied to individual captures. Then, the maps are recombined based on a set of stitching parameters to reinforced salient features present in objects at the FoV overlap regions. Simulations of the two models are conducted and demonstrated for performance evaluation

Salient region detection using contrast-based saliency and watershed segmentation

Salient region detection is useful for many applications such as image segmentation, compression, image retrieval, object tracking, and machine vision systems.In this paper, an approach to detect salient regions in a visual scene using contrast-based saliency and watershed segmentation is presented.The approach allows salient objects to be detected and extracted for analysis while preserving the actual boundaries of the salient objects. The approach can be executed in parallel making it efficient for real time applications

Multi-scale Discriminant Saliency with Wavelet-based Hidden Markov Tree Modelling

The bottom-up saliency, an early stage of humans' visual attention, can be considered as a binary classification problem between centre and surround classes. Discriminant power of features for the classification is measured as mutual information between distributions of image features and corresponding classes . As the estimated discrepancy very much depends on considered scale level, multi-scale structure and discriminant power are integrated by employing discrete wavelet features and Hidden Markov Tree (HMT). With wavelet coefficients and Hidden Markov Tree parameters, quad-tree like label structures are constructed and utilized in maximum a posterior probability (MAP) of hidden class variables at corresponding dyadic sub-squares. Then, a saliency value for each square block at each scale level is computed with discriminant power principle. Finally, across multiple scales is integrated the final saliency map by an information maximization rule. Both standard quantitative tools such as NSS, LCC, AUC and qualitative assessments are used for evaluating the proposed multi-scale discriminant saliency (MDIS) method against the well-know information based approach AIM on its released image collection with eye-tracking data. Simulation results are presented and analysed to verify the validity of MDIS as well as point out its limitation for further research direction.Comment: arXiv admin note: substantial text overlap with arXiv:1301.396

Modular dynamic RBF neural network for face recognition

Over the years, we have seen an increase in the use of RBF neural networks for the task of face recognition. However, the use of second order algorithms as the learning algorithm for all the adjustable parameters in such networks are rare due to the high computational complexity of the calculation of the Jacobian and Hessian matrix. Hence, in this paper, we propose a modular structural training architecture to adapt the Levenberg-Marquardt based RBF neural network for the application of face recognition. In addition to the proposal of the modular structural training architecture, we have also investigated the use of different front-end processors to reduce the dimension size of the feature vectors prior to its application to the LM-based RBF neural network. The investigative study was done on three standard face databases; ORL, Yale and AR databases