In this paper a review on biometric person identification has been discussed using features from retinal fundus image. Retina recognition is claimed to be the best person identification method among the biometric recognition systems as the retina is practically impossible to forge. It is found to be most stable, reliable and most secure among all other biometric systems. Retina inherits the property of uniqueness and stability. The features used in the recognition process are either blood vessel features or non-blood vessel features. But the vascular pattern is the most prominent feature utilized by most of the researchers for retina based person identification. Processes involved in this authentication system include pre-processing, feature extraction and feature matching. Bifurcation and crossover points are widely used features among the blood vessel features. Non-blood vessel features include luminance, contrast, and corner points etc. This paper summarizes and compares the different retina based authentication system. Researchers have used publicly available databases such as DRIVE, STARE, VARIA, RIDB, ARIA, AFIO, DRIDB, and SiMES for testing their methods. Various quantitative measures such as accuracy, recognition rate, false rejection rate, false acceptance rate, and equal error rate are used to evaluate the performance of different algorithms. DRIVE database provides 100% recognition for most of the methods. Rest of the database the accuracy of recognition is more than 90%.
Biometric identification systems, i.e. the systems that are able to recognize humans by analyzing their physiological or behavioral characteristics, have gained a lot of interest in recent years. They can be used to raise the security level in certain institutions or can be treated as a convenient replacement for PINs and passwords for regular users. Automatic face recognition is one of the most popular biometric technologies, widely used even by many low-end consumer devices such as netbooks. However, even the most accurate face identification algorithm would be useless if it could be cheated by presenting a photograph of a person instead of the real face. Therefore, the proper liveness measurement is extremely important. In this paper we present a method that differentiates between video sequences showing real persons and their photographs. First we calculate the optical flow of the face region using the Farnebäck algorithm. Then we convert the motion information into images and perform the initial data selection. Finally, we apply the Support Vector Machine to distinguish between real faces and photographs. The experimental results confirm that the proposed approach could be successfully applied in practice.
Development of facial recognition or expression recognition algorithms requires input data to thoroughly test the performance of algorithms in various conditions. Researchers are developing various methods to face challenges like illumination, pose and expression changes, as well as facial disguises. In this paper, we propose and establish a dataset of thermal facial images, which contains a set of neutral images in various poses as well as a set of facial images with different posed expressions collected with a thermal infrared camera. Since the properties of face in the thermal domain strongly depend on time, in order to show the impact of aging, collection of the dataset has been repeated and a corresponding set of data is provided. The paper describes the measurement methodology and database structure. We present baseline results of processing using state-of-the-art facial descriptors combined with distance metrics for thermal face reidentification. Three selected local descriptors, a histogram of oriented gradients, local binary patterns and local derivative patterns are used for elementary assessment of the database. The dataset offers a wide range of capabilities – from thermal face recognition to thermal expression recognition.
Annual and interannual phenomena and canopy behavior of prickly comfrey (Symphytum asperum Lep.) were studied in a 10-year experiment with 25 measurement sessions during the growing season. The results confirm the importance of long-term experiments in studying plant phenomena, biometrics and behavior. Prickly comfrey produced a green canopy each year and growth started very early in spring. Maximum plant height was less than 160 cm. Annual phenomena (growth initiation, seedling phase, flower phase, seed phase, senescent phase), interannual phenomena (initiation and youth, reproduction, new generation formation, plant death) and two population cycles (colonization and expansion) were measured. The duration of annual development up to canopy death can be expressed as x+2x+3x+2x, where x is initial growth. The genetic structure and activity of prickly comfrey promotes generative development of the species. Its age can be measured over a single and several vegetation generations. The ability to change the angle of vertical stem growth after 9 weeks can be considered a functional behavior of prickly comfrey and part of its life strategy. The differences between the organs in the upper and lower parts are very considerable and should be taken into account in morphological descriptions of this species. The upper and lower stems and leaves showed differential growth. Both stem and leaves were densely setose. Old leaves were 3.8 times longer, 4 times broader and 2.4 times thicker than young leaves. Hairs were on average 3 times longer on old than on young leaves. Flowers had contact with pollinators making relatively long visits to them.