Biometrics refers to identify people through their physical characteristics or behavior such as fingerprints, face, DNA,
hand geometries, retina and iris patterns. Typically, the iris pattern is to acquire in short distance to recognize a person,
however, in the past few years is a challenge identify a person by its iris pattern at certain distance in non-cooperative
environments. This challenge comprises: 1) high quality iris image, 2) light variation, 3) blur reduction, 4) specular
reflections reduction, 5) the distance from the acquisition system to the user, and 6) standardize the iris size and the density
pixel of iris texture. The solution of the challenge will add robustness and enhance the iris recognition rates. For this
reason, we describe the technical issues that must be considered during iris acquisition. Some of these considerations are
the camera sensor, lens, the math analysis of depth of field (DOF) and field of view (FOV) for iris recognition. Finally,
based on this issues we present experiment that show the result of captures obtained with our camera at distance and
captures obtained with cameras in very short distance.
The video annotation is important for web indexing and browsing systems. Indeed, in order to evaluate the performance of video query and mining techniques, databases with concept annotations are required. Therefore, it is necessary generate a database with a semantic indexing that represents the digital content of the Mexican bullfighting atmosphere. This paper proposes a scheme to make complex annotations in a video in the frame of multimedia search engine project. Each video is partitioned using our segmentation algorithm that creates shots of different length and different number of frames. In order to make complex annotations about the video, we use ELAN software. The annotations are done in two steps: First, we take note about the whole content in each shot. Second, we describe the actions as parameters of the camera like direction, position and deepness. As a consequence, we obtain a more complete descriptor of every action. In both cases we use the concepts of the TRECVid 2014 dataset. We also propose new concepts. This methodology allows to generate a database with the necessary information to create descriptors and algorithms capable to detect actions to automatically index and classify new bullfighting multimedia content.
Multimedia content production and storage in repositories are now an increasingly widespread practice. Indexing concepts for search in multimedia libraries are very useful for users of the repositories. However the search tools of content-based retrieval and automatic video tagging, still do not have great consistency. Regardless of how these systems are implemented, it is of vital importance to possess lots of videos that have concepts tagged with ground truth (training and testing sets). This paper describes a novel methodology to make complex annotations on video resources through ELAN software. The concepts are annotated and related to Mexican nature in a High Level Features (HLF) from development set of TRECVID 2014 in a collaborative environment. Based on this set, each nature concept observed is tagged on each video shot using concepts of the TRECVid 2014 dataset. We also propose new concepts, -like tropical settings, urban scenes, actions, events, weather, places for name a few. We also propose specific concepts that best describe video content of Mexican culture. We have been careful to get the database tagged with concepts of nature and ground truth. It is evident that a collaborative environment is more suitable for annotation of concepts related to ground truth and nature. As a result a Mexican nature database was built. It also is the basis for testing and training sets to automatically classify new multimedia content of Mexican nature.