ШАГ В НАУКУ - 2016
I Международная научная дистанционная студенческая конференция

Юридические науки
Face detection fields and part of it in face recognition areas
Хамидов С. 1

1. Институт законоведения и управления ВПА, г. Тула


Процесс совершенствования российского законодательства затрагивает большинство правовых институтов и требует коренной перестройки действующих нормативно-правовых актов. Тема безопасности организации и сохранности данных сегодня актуальна и востребована, как никогда прежде: Российская Федерация находится в тройке лидеров в рейтинге стран с самым высоким уровнем киберугроз.

Ключевые слова: защита от киберугроз




Xamidov S., student.

Institute of jurisprudence and management

of RPA, Russia

Scientific director: Kalinina Olga,

PhD in pedagogic,

head of the staff  management Department,

Institute of jurisprudence and management

of RPA, Russia


Nowadays some applications of Face Recognition don’t require face detection. In some cases, face images stored in the data bases are already normalized. There is a standard image input format, so there is no need for a detection step. An example of this could be a criminal data base. Face detection must deal with several well-known challenges. They are usually present in images captured in uncontrolled environments, such as surveillance video systems. These challenges can be attributed to some factors: Pose variation. The ideal scenario for face detection would be one in which only frontal images were involved. But, as stated, this is very unlikely in general uncontrolled conditions. Moreover, the performance of face detection algorithms drops severely when there are large pose variations. It’s a major research issue. Pose variation can happen due to subject’s movements or camera’s angle.

Approaches to face detection. It’s not easy to give a taxonomy of face detection methods. There isn’t a globally accepted grouping criteria. They usually mix and overlap. In this section, two classification criteria will be presented. One of them differentiates between distinct scenarios. Depending on these scenarios different approaches may be needed. The other criteria divides the detection algorithms into four categories.

Images in motion. Real time video gives the chance to use motion detection to localize faces. Nowadays, most commercial systems must locate faces in videos. There is a continuing challenge to achieve the best detecting results with the best possible performance. This classification can be made as follows [1]:

  •  Knowledge-based methods. Ruled-based methods that encode our knowledge of human faces.
  • Feature-invariant methods. Algorithms that try to find invariant features of a face despite its angle or position.
  • Template matching methods. These algorithms compare input images with stored patterns of faces or features.
  • Appearance-based methods. A template matching method whose pattern database is learnt from a set of training images.

Feature selection is a NP-hard problem, so researchers make afford towards a satisfactory algorithm, rather than an optimum one. The idea is to create an algorithm that selects the most satisfying feature subset, minimizing the dimensionality and complexity. Some approaches have used resemblance coefficient or satisfactory rate as a criterion and quantum genetic algorithm (QGA).



  1. Martishkin, S.V. Hranenie: uchebnoe posobie / S.V. Martishkin, Y.S. Povarov, V.D. Ruzanova. – Samara, 2003. – p.45.







Библиографическая ссылка

Хамидов С. Face detection fields and part of it in face recognition areas // . – . – № ;
URL: step-science-bip.csrae.ru/ru/0-94 (дата обращения: 29.11.2020).

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