This method significantly increases the number selleck chemicals llc of imposter samples without the need of collecting them separately. On the other hand, an experiment that offers the flexibility of input data may require more efforts to collect additional test data [85, 91]. Having said that, user defined input resembles closer to real world scenario than fixed text. Furthermore, it is infeasible to constrain the input text in some cases such as [22, 72, 74], due to the nature and objective of the experiment where the user must have the freedom of input. Therefore, the number of research works on both types of inputs is fairly even.4.4. Genuine and Imposter SamplesData collected will eventually be used for performance evaluation. The most common way of performance measurement is the degree of accuracy of a system’s ability to distinguish genuine and imposter.

Imposter samples are usually obtained by either the same individual who contributes to the generation of genuine samples in database [92] or via another group of individuals attacking or simulating the genuine samples stored in the database [22]. The former imposes participants to provide more inputs and devote more time in the experiment. The lengthy process may deter volunteer participation. On the other hand, the latter required less participation effort by each user but a separate pool is required. Difficult
Recently, as the rapid development of digital imaging technology, digital imaging devices have been widely applied in many fields, including computational photography, security monitoring, robot navigation, and military reconnaissance.

However, video signals are often contaminated by all kinds of noise during acquisition and transmission, such as optical noise, component noise, sensor noise, and circuit noise. The noise in video signals not only damages the original information and results in unpleasant visual effect, but also affects the effectiveness of further coding or processing such as feature extraction, object detection, motion tracking, and pattern recognition. So, noise reduction in contaminated video sequences should be implemented.Many video denoising methods have been proposed in the past decade, most of which perform in the spatial domain, temporal domain, or their combination [1�C6]. Methods in spatial domain often produce limited results because they do not take advantage of spatiotemporal correlations of neighboring frames.

Methods in temporal domain consider Anacetrapib the correlations of neighboring frames, but they are only appropriate for still video. Additionally, the results have artifacts or smear phenomenon when objects motion exist. By combining the spatial domain with temporal domain, impressive results can be produced. However, these methods generally require a huge amount of computation.