H2020Индивидуална стипендия2015–2017

StillNoFace · Identity matching from still images without face information

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период
2015-09-01 → 2017-08-31
Финансиране от ЕС
168 392 €
Участници
2
Схема
MSCA-IF-GF

Линиите свързват координатора с партньорите.

Накратко на български

Разпознаването на идентичността на човек от снимки се изследва чрез анализ на тялото и визуални белези, като например височина, пол или облекло. Това е важно, когато лицето е покрито или не се вижда, което затруднява стандартните системи за сигурност.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Identity matching from still images without face information

"Problem statement In computer vision, human identity matching from images and/or video has been an active research topic for more than two decades and its popularity is increasing with the increase in computing power. Integrating soft biometrics such as gender, height, weight, age, and ethnicity to a primary biometrics system (e.g., face) has been studied. In most of the existing methods, the problem of human classification assisted by soft biometrics has been approached using facial information. However, in real-life scenarios, such information might not be available (e.g., the face might be covered or occluded). This led to methods that employ information from the human body to perform human identification and tracking based on soft biometrics. Moreover, when we are interested in providing a description of an object or a human, we tend to use visual attributes to accomplish this task. For example, a laptop can have a wide screen, a silver color, and a brand logo, whereas a human can be tall, female, wearing a blue t-shirt and carrying a backpack. Visual attributes in computer vision are equivalent to the adjectives in our speech. We rely on visual attributes since they are a meaningful semantic representation of objects or humans that can be understood by both computers and humans. However, effectively predicting the corresponding visual attributes of a human given an image remains a challenging task. Objectives In this research project, we propose methods for predicting a person’s identity from images without facial information based on soft biometrics and visual attributes. Having as input a still image or a video showing only the body of an individual in the wild, the overall objectives of the project are: • Estimate the gender and soft biometrics of the individual, such as his/her weight and height • Retrieve images of individuals with specific visual attributes, such as ""wearing a hat"", ""sitting on a chair"" Benefit for the society A major application of the outcome of the project is the automated recognition of individuals from images captured by standard cameras in order to allow them to enter to their house or office or to control a car. Moreover, a prominent category of applications involves security and safety in public and private spaces (e.g. airports, train stations, concert halls). In these places, surveillance cameras generally do not provide facial information as the individual’s image may be acquired from the behind. Besides, when it is available, face information may be of low resolution, thus difficult to extract any useful information from it. "

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

In computer vision, human identity matching from images and/or video has been an active research topic for more than two decades and its popularity is increasing with the increase in computing power. The state of the art techniques are based on face images and gait recognition from long video sequences. However, in many real applications only some static images of the subject may be available where face information is missing (e.g. posterior views). These scenarios have not been addressed by the research community as they are difficult to handle. In this action, we propose a method for matching identities from a set of 2D images of a person without any facial information. The method consists of two steps: at first, the human body is modelled by a 3D articulated model whose pose is estimated by its 2D projections onto the images. Then, biometric features are computed by fitting 3D deformable models to the image data, thus capturing the form and size of the main parts of the anatomy. The overall framework works under a probabilistic framework, with a learning step, in order to encode pose and anatomy variations between a set of individuals that are to be identified.

Оригинален текст от CORDIS (на английски).

Участници

Връзки

Данни: CORDIS, © Европейски съюз