A Face Detection System Based Skin Color and Neural Network

2008 ◽  
Author(s):  
Wang Zhanjie ◽  
Tong Li
Keyword(s):  
Neural Network ◽  
Face Detection ◽  
Skin Color ◽  
Detection System

A LabVIEW Design for Frontal and Non-Frontal Human Face Detection System in Complex Background

2014 ◽  
Vol 490-491 ◽  
pp. 1259-1266 ◽  
Author(s):  
Muralindran Mariappan ◽  
Manimehala Nadarajan ◽  
Rosalyn R. Porle ◽  
Vigneswaran Ramu ◽  
Brendan Khoo Teng Thiam
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Detection Method ◽  
Detection System ◽  
Detection Algorithm ◽  
Biometric Technology ◽  
Main Challenge ◽  
Human Face Detection ◽  
Color Detection ◽  
Large Face

Biometric identification has advanced vastly since many decades ago. It became a blooming area for research as biometric technology has been used extensively in areas like robotics, surveillance, security and others. Face technology is more preferable due to its reliability and accuracy. By and large, face detection is the first processing stage that is performed before extending to face identification or tracking. The main challenge in face detection is the sensitiveness of the detection to pose, illumination, background and orientation. Thus, it is crucial to design a face detection system that can accommodate those problems. In this paper, a face detection algorithm is developed and designed in LabVIEW that is flexible to adapt changes in background and different face angle. Skin color detection method blending with edge and circle detection is used to improve the accuracy of face detected. The overall system designed in LabVIEW was tested in real time and it achieves accuracy about 97%.

scholarly journals Robot movement controller based on dynamic facial pattern recognition

2021 ◽  
Vol 22 (2) ◽  
pp. 733
Author(s):  
Siti Nurmaini ◽  
Ahmad Zarkasi ◽  
Deris Stiawan ◽  
Bhakti Yudho Suprapto ◽  
Sri Desy Siswanti ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Face Detection ◽  
Detection System ◽  
Accuracy Rate ◽  
True Value ◽  
The Face ◽  
Facial Pattern ◽  
Lower Frame ◽  
The Right

In terms of movement, mobile robots are equipped with various navigation techniques. One of the navigation techniques used is facial pattern recognition. But Mobile robot hardware usually uses embedded platforms which have limited resources. In this study, a new navigation technique is proposed by combining a face detection system with a ram-based artificial neural network. This technique will divide the face detection area into five frame areas, namely top, bottom, right, left, and neutral. In this technique, the face detection area is divided into five frame areas, namely top, bottom, right, left, and neutral. The value of each detection area will be grouped into the ram discriminator. Then a training and testing process will be carried out to determine which detection value is closest to the true value, which value will be compared with the output value in the output pattern so that the winning discriminator is obtained which is used as the navigation value. In testing 63 face samples for the Upper and Lower frame areas, resulting in an accuracy rate of 95%, then for the Right and Left frame areas, the resulting accuracy rate is 93%. In the process of testing the ram-based neural network algorithm pattern, the efficiency of memory capacity in ram, the discriminator is 50%, assuming a 16-bit input pattern to 8 bits. While the execution time of the input vector until the winner of the class is under milliseconds (ms).

scholarly journals Face Detection in a Mixed-Subject Document

2016 ◽  
Vol 6 (6) ◽  
pp. 2828
Author(s):  
Lhoussaine Bouhou ◽  
Rachid El Ayachi ◽  
Mohamed Baslam ◽  
Mohamed Oukessou
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Template Matching ◽  
Detection System ◽  
Second Phase ◽  
Text And Image ◽  
Important Research Topic ◽  
The Face ◽  
Second Category ◽  
Text Images

<p>Before you recognize anyone, it is essential to identify various characteristics variations from one person to another. among of this characteristics, we have those relating to the face. Nowadays the detection of skin regions in an image has become an important research topic for the location of a face in the image. In this research study, unlike previous research studies  related  to  this  topic  which  have  focused  on  images  inputs  data  faces,  we  are  more interested to the fields face detection in mixed-subject documents (text + images). The face detection system developed is based on the hybrid method to distinguish two categories of objects from the mixed document. The first category is all that is text or images containing figures having no skin color, and the second category is any figure with the same color as the skin. In the second phase the detection system is based on Template Matching method to distinguish among the figures of the second category only those that contain faces to detect them. To validate this study, the system developed is tested on the various documents which including text and image.</p>

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.

scholarly journals Identifikasi Wajah Dengan Segmentasi Warna Kulit Menggunakan Metode Viola Jones

2019 ◽  
Vol 4 (1) ◽  
pp. 1-6
Author(s):  
Andi Asni b ◽  
Tamara Octa Dana
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Skin Color ◽  
Detection System ◽  
Color Segmentation ◽  
Face Images ◽  
Detection Of Objects ◽  
The Face ◽  
Left And Right ◽  
Normal Light

Abstract - Face detection (face detection) is one of the initial steps that is very important before the face recognition process (face recognition). Face detection is the detection of objects in the form of faces in which there are special features that represent the shape of faces in general. One method of face detection is the Viola Jones method. Viola Jones method is used to detect faces and skin color segmentation, test data processing using Matlab and capture on a Smartphone. The test is carried out at normal light intensity with a predetermined distance and face position. The results of this study indicate the level of accuracy of detection of face image variations in the position of face images facing forward (frontal), sideways left and right 45̊. But it has a weakness of this face detection system that is unable to determine faces in images that have faces that are not upright (tilted) or not frontal (facing sideways) at a 90̊ angle. Face position that is upright / not upright will determine the success of this face detection. The level of identification of the Viola Jones simulation was 100% with 4 images consisting of 3 boys and 1 girl.

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