Image Acquisition System based on Synchronized High Resolution Gigabit Ethernet Cameras

Over the last few years, the huge rise in various computer vision applications canbe observed. They are widely used in such areas like video surveillance, medicaldiagnostics, biometrics recognition, the automotive or military industries. Mostof these solutions take advantage of high-resolution cameras in order to obtainhigh quality images. Surprisingly, little attention is paid in the literature tothe practical implementation of off-the-shelf image acquisition systems. Mostavailable solutions are composed of custom developed electronic devices whichuse specialized multi-core DSPs and / or FPGA technology. Therefore, in thispaper the novel realization of the scalable and comprehensive image acquisitionsystem based on synchronized high resolution Gigabit Ethernet camerasis presented. The proposed solution allows the connection of multiple camerastogether with any number of external illumination modules. Selected devicescan be synchronized with each other in user-defined configurations. Hence,designed solution can be easily integrated in both simple and complex applications.Authors describe in detail design and implementation processes of theproposed platform. The performance issues that can occur in such systems arepresented and discussed. Obtained results are encouraging and useful for thedevelopment of similar solutions

COMPACT: biometric dataset of face images acquired in uncontrolled indoor environment

Biometric databases are important components that help to improve state-of-the-art recognition performance. The availability of more and more difficult data attracts the researchers' attention, who systematically develop novel recognition algorithms and increase identification accuracy. Surprisingly, most of the popular face datasets, like LFW or IJBA are not fully unconstrained. The majority of the available images were not acquired on-the-move, which reduces the amount of blur caused by motion or incorrect focusing. Therefore, in this paper, the COMPACT database for studying less-cooperative face recognition is introduced. The dataset consists of high-resolution images of 108 subjects acquired in a fully automated manner as people go through the recognition gate. This ensures that the collected data contains the real world degradation factors: different distances, expressions, occlusions, pose variations and motion blur. Additionally, the authors conducted a series of experiments that verify face recognition performance on the collected data

COMPACT: biometric dataset of face images acquired in uncontrolled indoor environment

Biometric databases are important components that help improve the performanceof state-of-the-art recognition applications. The availability of more andmore challenging data is attracting the attention of researchers, who are systematicallydeveloping novel recognition algorithms and increasing the accuracyof identification. Surprisingly, most of the popular face datasets (like LFW orIJBA) are not fully unconstrained. The majority of the available images werenot acquired on-the-move, which reduces the amount of blurring that is causedby motion or incorrect focusing. Therefore, the COMPACT database for studyingless-cooperative face recognition is introduced in this paper. The datasetconsists of high-resolution images of 108 subjects acquired in a fully automatedmanner as people go through the recognition gate. This ensures that the collecteddata contains real-world degradation factors: different distances, expressions,occlusions, pose variations, and motion blur. Additionally, the authorsconducted a series of experiments that verified the face-recognition performanceon the collected data

COMPACT: biometric dataset of face images acquired in uncontrolled indoor environment

Biometric databases are important components that help improve the performanceof state-of-the-art recognition applications. The availability of more andmore challenging data is attracting the attention of researchers, who are systematicallydeveloping novel recognition algorithms and increasing the accuracyof identification. Surprisingly, most of the popular face datasets (like LFW orIJBA) are not fully unconstrained. The majority of the available images werenot acquired on-the-move, which reduces the amount of blurring that is causedby motion or incorrect focusing. Therefore, the COMPACT database for studyingless-cooperative face recognition is introduced in this paper. The datasetconsists of high-resolution images of 108 subjects acquired in a fully automatedmanner as people go through the recognition gate. This ensures that the collecteddata contains real-world degradation factors: different distances, expressions,occlusions, pose variations, and motion blur. Additionally, the authorsconducted a series of experiments that verified the face-recognition performanceon the collected data