Crónicas del Gran Capitán

Copia digital. Valladolid : Junta de Castilla y León. Consejería de Cultura y Turismo, 2009-201

On Using High-Definition Body Worn Cameras for Face Recognition from a Distance

Recognition of human faces from a distance is highly desirable for law-enforcement. This paper evaluates the use of low-cost, high-definition (HD) body worn video cameras for face recognition from a distance. A comparison of HD vs. Standard-definition (SD) video for face recognition from a distance is presented. HD and SD videos of 20 subjects were acquired in different conditions and at varying distances. The evaluation uses three benchmark algorithms: Eigenfaces, Fisherfaces and Wavelet Transforms. The study indicates when gallery and probe images consist of faces captured from a distance, HD video result in better recognition accuracy, compared to SD video. This scenario resembles real-life conditions of video surveillance and law-enforcement activities. However, at a close range, face data obtained from SD video result in similar, if not better recognition accuracy than using HD face data of the same range

Colegio en la alameda del río Eresma, Segovia [Hojas Resumen]

Colegio en la alameda del río Eresma, Segovi

The BANCA database and evaluation protocol, in

Abstract. In this paper we describe the acquisition and content of a new large, realistic and challenging multi-modal database intended for training and testing multi-modal verification systems. The BANCA database was captured in four European languages in two modalities (face and voice). For recording, both high and low quality microphones and cameras were used. The subjects were recorded in three different scenarios, controlled, degraded and adverse over a period of three months. In total 208 people were captured, half men and half women. In this paper we also describe a protocol for evaluating verification algorithms on the database. The database will be made available to the research community throug

Survey on automatic lip-reading in the era of deep learning

In the last few years, there has been an increasing interest in developing systems for Automatic Lip-Reading (ALR). Similarly to other computer vision applications, methods based on Deep Learning (DL) have become very popular and have permitted to substantially push forward the achievable performance. In this survey, we review ALR research during the last decade, highlighting the progression from approaches previous to DL (which we refer to as traditional) toward end-to-end DL architectures. We provide a comprehensive list of the audio-visual databases available for lip-reading, describing what tasks they can be used for, their popularity and their most important characteristics, such as the number of speakers, vocabulary size, recording settings and total duration. In correspondence with the shift toward DL, we show that there is a clear tendency toward large-scale datasets targeting realistic application settings and large numbers of samples per class. On the other hand, we summarize, discuss and compare the different ALR systems proposed in the last decade, separately considering traditional and DL approaches. We address a quantitative analysis of the different systems by organizing them in terms of the task that they target (e.g. recognition of letters or digits and words or sentences) and comparing their reported performance in the most commonly used datasets. As a result, we find that DL architectures perform similarly to traditional ones for simpler tasks but report significant improvements in more complex tasks, such as word or sentence recognition, with up to 40% improvement in word recognition rates. Hence, we provide a detailed description of the available ALR systems based on end-to-end DL architectures and identify a tendency to focus on the modeling of temporal context as the key to advance the field. Such modeling is dominated by recurrent neural networks due to their ability to retain context at multiple scales (e.g. short- and long-term information). In this sense, current efforts tend toward techniques that allow a more comprehensive modeling and interpretability of the retained context.This work is partly supported by the Spanish Ministry of Economy and Competitiveness, Spain under project grant TIN2017-90124-P, the Ramon y Cajal Programme, the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502), and the Kristina project funded by the European Union Horizon 2020 - Research and Innovation Framework Programme under grant agreement No. 645012