scholarly journals Face Detection and Recognition Using Back Propagation Neural Network and Fourier Gabor Filters

2011 ◽  
Vol 2 (3) ◽  
pp. 15-21 ◽  
Author(s):  
Anissa Bouzalmat ◽  
Naouar Belghini ◽  
Arsalane Zarghili ◽  
Jamal Kharroubi
Keyword(s):  
Neural Network ◽  
Face Detection ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Gabor Filters ◽  
Face Detection And Recognition ◽  
Detection And Recognition

Multi-Angle Face Detection Using Back Propagation Neural Network

2011 ◽  
Vol 121-126 ◽  
pp. 2411-2415
Author(s):  
Kamarul Hawari Bin Ghazali ◽  
Jie Ma ◽  
Rui Xiao
Keyword(s):  
Neural Network ◽  
Face Detection ◽  
System Development ◽  
Vision System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Machine Vision Application ◽  
Side Face ◽  
Fatigue Detection ◽  
Vision Application

In machine vision application, the main part to analyze an image is to identify its features which contribute to efficiency of the system. Many applications in vision system and image analysis used face detection as a feature of their whole system development. In application such as video surveillance, fatigue detection and security system, face is a fundamental step in the analysis before proceed to system implementation. It is very challenging to recognize a face from an image due to the wide variety of face and the uncertain of face position. In this paper, we propose a neural network based approach to identify multi-angle face which falls into five categories: all left-side face, half left-side face, positive face, half right-side face, and all right-side face. More than 100 images of each category have been used for training and testing of face detection and its features was extracted to be an input to BP neural network. We analyzed the result of training and testing set of neural network and the best classification achieved was 90.7%.

scholarly journals Sparse Neural Network for horse face detection in a Smart Riding Club Biometric System

2021 ◽  
Author(s):  
Islem Jarraya ◽  
Wael Ouarda ◽  
Fatma BenSaid ◽  
Adel Alimi
Keyword(s):  
Neural Network ◽  
Face Detection ◽  
Detection System ◽  
Recognition Rate ◽  
Biometric System ◽  
Training Time ◽  
Face Detection And Recognition ◽  
Global And Local ◽  
Sparse Features ◽  
Detection And Recognition

Horses and breeders need to be safe on the farm and the riding club. On account of the great value of the horse, the breeder needs to protect it from theft and disease. In this context, it is important to detect and to recognize the identity of each horse for security reasons. In fact, this paper proposes a Smart Riding Club Biometric System (SRCBS) consisting in automatically detecting and recognizing horses as well as humans. The proposed system is based on the facial biometrics for a horse and the gait biometrics for a human due to their simplicity and intuitiveness in an uncontrolled environment. The present work focuses mainly on horse face detection and recognition. Animal face detection is still extremely difficult given the fact that face textures and shapes are grossly diverse. In addition, recent detectors require a huge dataset for training and represent a huge number of parameters and layers, leading to so much training time. For resolving these problems and also for a useful detection system, this paper proposes a Sparse Neural Network (SNN) based on sparse features for horse face detection.<br>Different global and local features were performed to identify horses and humans for the recognition process. Due to the unavailability of horse databases, this paper presents a new benchmark for horse detection and recognition in order to evaluate our proposed system. This system achieved an average precision equal to 90% for horse face detection and a recognition rate equal to 99.89% for horse face identification.

Neural Networks and 3D Edge Genetic Template Matching for Real-Time Face Detection and Recognition

2011 ◽  
pp. 164-177
Author(s):  
Stephen Karungaru ◽  
Minoru Fukumi ◽  
Norio Akamatsu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Real Time ◽  
Face Detection ◽  
Template Matching ◽  
Neural Network Learning ◽  
Face Detection And Recognition ◽  
The One ◽  
Detection And Recognition

This chapter describes a novel system that can track and recognize faces in real time using neural networks and genetic algorithms. The main feature of this system is a 3D facemask that combined with a neural network based face detector and adaptive template matching using genetic algorithms, is capable of detecting and recognizing faces in real time. Neural network learning and template matching enable size and pose invariant face detection and recognition while the genetic algorithm optimizes the searching algorithms enabling real time usage of the system. It is hoped that this chapter will show how and why neural networks and genetic algorithms are well suited to solve complex pattern recognition problems like the one presented in this chapter.

scholarly journals C# solutions for a face detection and recognition system

2007 ◽  
Vol 20 (1) ◽  
pp. 93-105
Author(s):  
Catalin-Daniel Caleanu ◽  
Corina Botoca
Keyword(s):  
Neural Network ◽  
Face Detection ◽  
Object Oriented ◽  
Recognition System ◽  
Video Stream ◽  
Recognition Phase ◽  
Key Issues ◽  
Face Detection And Recognition ◽  
Artificial Neural Network Ann ◽  
Detection And Recognition

Key issues on using a new programming language - C# - in implementation of a face detection and recognition (FDR) system are presented. Mainly the following aspects are detailed: how to acquire an image, broadcast a video stream, manipulate a database, and finally, the detection/recognition phase all in relation with theirs possible C#/.NET solutions. Emphasis was placed on artificial neural network (ANN) methods for face detection/recognition along with C# object oriented implementation proposal.

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