A robust illumination-invariant face recognition based on fusion of thermal IR, maximum filter and visible image

Face recognition has many challenges especially in real life detection, whereby to maintain consistency in getting an accurate recognition is almost impossible. Even for well-established state-of-the-art algorithms or methods will produce low accuracy in recognition if it was conducted under poor or bad lighting. To create a more robust face recognition with illumination invariant, this paper proposed an algorithm using a triple fusion approach. We are also implementing a hybrid method that combines the active approach by implementing thermal infrared imaging and also the passive approach of Maximum Filter and visual image. These approaches allow us to improve the image pre-processing as well as feature extraction and face detection, even if we capture a person’s face image in total darkness. In our experiment, Extended Yale B database are tested with Maximum Filter and compared against other state-of-the-filters. We have conduct-ed several experiments on mid-wave and long-wave thermal Infrared performance during pre-processing and saw that it is capable to improve recognition beyond what meets the eye. In our experiment, we found out that PCA eigenface cannot be produced in a poor or bad illumination. Mid-wave thermal creates the heat signature in the body and the Maximum Filter maintains the fine edges that are easily used by any classifiers such as SVM, OpenCV or even kNN together with Euclidian distance to perform face recognition. These configurations have been assembled for a face recognition portable robust system and the result showed that creating fusion between these processed image illumination invariants during preprocessing show far better results than just using visible image, thermal image or maximum filtered image separately

Non-Rigid Registration via Global to Local Transformation

Non-rigid point set and image registration are key problems in plenty of computer vision and pattern recognition tasks. Typically, the non-rigid registration can be formulated as an optimization problem. However, registration accuracy is limited by local optimum. To solve this problem, we propose a method with global to local transformation for non-rigid point sets registration and it also can be used to infrared (IR) and visible (VIS) image registration. Firstly, an objective function based on Gaussian fields is designed to make a problem of non-rigid registration transform into an optimization problem. A global transformation model, which can describe the regular pattern of non-linear deformation between point sets, is then proposed to achieve coarse registration in global scale. Finally, with the results of coarse registration as initial value, a local transformation model is employed to implement fine registration by using local feature. Meanwhile, the optimal global and local transformation models estimated from edge points of IR and VIS image pairs are used to achieve non-rigid image registration. The qualitative and quantitative comparisons demonstrate that the proposed method has good performance under various types of distortions. Moreover, our method can also produce accurate results of IR and VIS image registration