Review of Face Detection Systems Based Artificial Neural Networks Algorithms

Face detection is one of the most relevant applications of image processing and biometric systems. Artificial neural networks (ANN) have been used in the field of image processing and pattern recognition. There is lack of literature surveys which give overview about the studies and researches related to the using of ANN in face detection. Therefore, this research includes a general review of face detection studies and systems which based on different ANN approaches and algorithms. The strengths and limitations of these literature studies and systems were included also.Comment: 16 pages, 12 figures, 1 table, IJMA Journa

A Real-time Model for Multiple Human Face Tracking from Low-resolution Surveillance Videos

AbstractThis article discusses a novel approach of multiple-face tracking from low-resolution surveillance videos. There has been significant research in the field of face detection using neural-network based training. Neural network based face detection methods are highly accurate, albeit computationally intensive. Hence neural network based approaches are not suitable for real-time applications. The proposed approach approximately detects faces in an image solely using the color information. It detects skin region in an image and finds existence of eye and mouth region in the skin region. If it finds so, it marks the skin region as a face and fits an oriented rectangle to the face. The approach requires low computation and hence can be applied on subsequent frames from a video. The proposed approach is tested on FERET face database images, on different images containing multiple faces captured in unconstrained environments, and on frames extracted from IP surveillance camera

An Automatic Human Face Detection Method

This article contains a proposal for an automatic human face detection method, that tries to join several theories proposed by different authors. The method is based on detection of shape features (eye pairs) and skin color. The method assumes certain circumstances and constraints, respectively. Therefore it is not applicable universally. Given the constraints, it is effective enough for applications where fast execution is required. Test results are given and at the end some directives for future work are discussed

Модуль нейромережевого виділення обличчя у відеопотоці

Дипломний проєкт включає пояснувальну записку (68 сторінок, 4 додатки, 24 рисунки) Мета розробки – розробка комп’ютерного модулю для забезпечення ефективного нейромережевого виділення обличчя у відеопотоці. Нейронна мережа дозволяє: здійснювати навчання системи на даних зображень та розпізнавати обличчя в відеопотоці. Передбачена можливість користування системою через застосунок. В процесі розробки були використані технології мови C#. В ході розробки: - проведено аналіз методів побудови існуючих комп’ютерних нейромережевих систем виділення облич; - сформульовані вимоги до нейромережевої системи виділення облич в відеопотоці; - розроблено математичне забезпечення для нейромережевої системи виділення облич в відеопотоці; - розроблена структура нейромережевої системи виділення облич в відеопотоці; - проведено комп’ютерні експерименти, що довели ефективність розробленого модулю виділення; - розроблено застосунок для управління і моніторингу роботи нейромережевої системи виділення облич в відеопотоці; Впровадження цієї системи в своїх проектах дозволить використовувати виділення облич в своїх цілях.The diploma project includes an explanatory note (68 pages, 4 appendices, 24 figures). The aim of the development is to create a computer module for efficient neural network-based face detection in video streams. The neural network allows: training the system on image data and recognizing faces in a video stream. It is possible to use the system through the application. C# language technologies were used in the development process. During development: - an analysis of methods for constructing existing computer neural network systems for face recognition was carried out; - requirements for the neural network system for face detection in a video stream were formulated; - mathematical support was developed for the neural network-based face detection system in video streams; - the structure of the neural network system for face detection in a video stream was developed; - computer experiments were conducted to prove the effectiveness of the developed face detection module; - an application was developed for managing and monitoring the operation of the neural network system for face detection in a video stream; The implementation of this system in projects allows for using face detection for various purposes

Multi-stream gaussian mixture model based facial feature localization=Çoklu gauss karışım modeli tabanlı yüz öznitelikleri bulma algoritması

This paper presents a new facial feature localization system which estimates positions of eyes, nose and mouth corners simultaneously. In contrast to conventional systems, we use the multi-stream Gaussian mixture model (GMM) framework in order to represent structural and appearance information of facial features. We construct a GMM for the region of each facial feature, where the principal component analysis is used to extract each facial feature. We also build a GMM which represents the structural information of a face, relative positions of facial features. Those models are combined based on the multi-stream framework. It can reduce the computation time to search region of interest (ROI). We demonstrate the effectiveness of our algorithm through experiments on the BioID Face Database