NEAR INFRARED IMAGING FOR SUBCUTANEOUS VEINS LOCALIZATION USING WEARABLE OPTICAL IMAGING DEVICE

Intravenous (IV) catheterization is a basic need for medical treatment. Skilled trained medical practices such as doctor, nurse or even paramedic need to learn as this their basic knowledge. To perform this treatment, they need to locate veins they can get by visual or feel it with their fingers. This technique has its own downside as sometimes different patient have differ skin tone and deepness of their veins. Sometimes patients also get scars or even some of them have thick hair. To attempt venipuncture, sometimes they need to repeat it two or three times if it does not succeed. This is due to non-visibility to locate patient’s veins. This may result severe pain to the patient and leave a bigger impact to their health. These inaccurate catheter insertions need to be overcome with a device that can help medical practitioners to locate veins from patients easily and fast for venipuncture process. Today’s technology give human to look through human’s body but non of them have a capability to locate and display subcutaneous veins structure right in front of their eyes for a user to perform iv catheterization process. In this project, near infrared (NIR) imaging technique will be choose as it has several advantages in compared to the other techniqu

QUEST Hierarchy for Hyperspectral Face Recognition

Face recognition is an attractive biometric due to the ease in which photographs of the human face can be acquired and processed. The non-intrusive ability of many surveillance systems permits face recognition applications to be used in a myriad of environments. Despite decades of impressive research in this area, face recognition still struggles with variations in illumination, pose and expression not to mention the larger challenge of willful circumvention. The integration of supporting contextual information in a fusion hierarchy known as QUalia Exploitation of Sensor Technology (QUEST) is a novel approach for hyperspectral face recognition that results in performance advantages and a robustness not seen in leading face recognition methodologies. This research demonstrates a method for the exploitation of hyperspectral imagery and the intelligent processing of contextual layers of spatial, spectral, and temporal information. This approach illustrates the benefit of integrating spatial and spectral domains of imagery for the automatic extraction and integration of novel soft features (biometric). The establishment of the QUEST methodology for face recognition results in an engineering advantage in both performance and efficiency compared to leading and classical face recognition techniques. An interactive environment for the testing and expansion of this recognition framework is also provided