Face Detection Using Discrete Gabor Jets And Color Information

Face detection allows to recognize and detect human faces and provides information about their location in a given image. Many applications such as biometrics, face recognition, and video surveillance employ face detection as one of their main modules. Therefore, improvement in the performance of existing face detection systems and new achievements in this field of research are of significant importance. In this paper a hierarchical classification approach for face detection is presented. In the first step, discrete Gabor jets (DGJ) are used for extracting features related to the brightness information of images and a preliminary classification is made. Afterwards, a skin detection algorithm, based on modeling of colored image patches, is employed as a post- processing of the results of DGJ- based classification. It is shown that the use of color efficiently reduces the number of false positives while maintaining a high true positive rate. Finally, a comparison is made with the OpenCV implementation of the Viola and Jones face detector and it is concluded that higher correct classification rates can be attained using the proposed face detector

Webcam‐based system for video‐oculography

Video‐oculography (VOG) is a tool providing diagnostic information about the progress of the diseases that cause regression of the vergence eye movements, such as Parkinson's disease (PD). The majority of the existing systems are based on sophisticated infra‐red (IR) devices. In this study, the authors show that a webcam‐based VOG system can provide similar accuracy to that of a head‐mounted IR‐based VOG system. They also prove that the authors’ iris localisation algorithm outperforms current state‐of‐the‐art methods on the popular BioID dataset in terms of accuracy. The proposed system consists of a set of image processing algorithms: face detection, facial features localisation and iris localisation. They have performed examinations on patients suffering from PD using their system and a JAZZ‐novo head‐mounted device with IR sensor as reference. In the experiments, they have obtained a mean correlation of 0.841 between the results from their method and those from the JAZZ‐novo. They have shown that the accuracy of their visual system is similar to the accuracy of IR head‐mounted devices. In the future, they plan to extend their experiments to inexpensive high frame rate cameras which can potentially provide more diagnostic parameters