scholarly journals Building Face Detection with Face Divine Proportions

2021 ◽  
Vol 17 (04) ◽  
pp. 63
Author(s):  
Mohamed Oualla ◽  
Khalid Ounachad ◽  
Abdelalim Sadiq
Keyword(s):  
Face Detection ◽  
Face Image ◽  
Integral Image ◽  
Detection Rates ◽  
Aesthetic Medicine ◽  
Adaboost Algorithm ◽  
The World ◽  
High Detection ◽  
The Face ◽  
Human Faces

<p class="0abstract"><span lang="EN-US">In this paper, we proposed an algorithm for detecting multiple human faces in an image based on haar-like features to represent the invariant characteristics of a face. The choice of relevant and more representative features is based on the divine proportions of a face. This technique, widely used in the world of beauty, especially in aesthetic medicine, allows the face to be divided into a set of specific regions according to known mathematical measures. Then we used the Adaboost algorithm for the learning phase. All of our work is based on the Viola and Jones algorithm, in particular their innovative technique called Integral Image, which calculates the value of a Haar-Like feature extracted from a face image. In the rest of this article, we will show that our approach is promising and can achieve high detection rates of up to 99%.</span></p>

The Adaboost Algorithm Applied in ATM Automatic Identification System

2013 ◽  
Vol 753-755 ◽  
pp. 2941-2944
Author(s):  
Ming Hui Zhang ◽  
Yao Yu Zhang
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Computing Time ◽  
Automatic Identification ◽  
Identification System ◽  
Human Face ◽  
Detection Experiment ◽  
Adaboost Algorithm ◽  
Face Features ◽  
Human Faces

Seeing that human face features are unique, an increasing number of face recognition algorithms on existing ATM are proposed. Since face detection is a primary link of face recognition, our system adopts AdaBoost algorithm which is based on face detection. Experiment results demonstrated that the computing time of face detection using this algorithm is about 70ms, and the single and multiple human faces can be effectively measured under well environment, which meets the demand of the system.

scholarly journals An Innovative Face Detection Based on YCgCr Color Space

2018 ◽  
Author(s):  
Solly Aryza
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Color Image ◽  
Color Space ◽  
Gaussian Model ◽  
Detection Rates ◽  
Detection Algorithms ◽  
Lighting Conditions ◽  
Wide Range ◽  
Human Faces

It is very challenging to recognize a face from an image due to the wide variety of face and the uncertain of face position. The research on detecting human faces in color image and in video sequence has been attracted with more and more people. In this paper, we propose a novel face detection method that achieves better detection rates. The new face detection algorithms based on skin color model in YCgCr chrominance space. Firstly, we build a skin Gaussian model in Cg-Cr color space. Secondly, a calculation of correlation coefficient is performed between the given template and the candidates. Experimental results demonstrate that our system has achieved high detection rates and low false positives over a wide range of facial variations in color, position and varying lighting conditions.

Face and Eye Detection

2011 ◽  
pp. 5-44 ◽  
Author(s):  
Daijin Kim ◽  
Jaewon Sung
Keyword(s):  
Face Detection ◽  
Facial Expression Recognition ◽  
Face Image ◽  
Face Tracking ◽  
Expression Recognition ◽  
Human Face ◽  
Eye Detection ◽  
Face Image Analysis ◽  
Face Images ◽  
The Face

Face detection is the most fundamental step for the research on image-based automated face analysis such as face tracking, face recognition, face authentication, facial expression recognition and facial gesture recognition. When a novel face image is given we must know where the face is located, and how large the scale is to limit our concern to the face patch in the image and normalize the scale and orientation of the face patch. Usually, the face detection results are not stable; the scale of the detected face rectangle can be larger or smaller than that of the real face in the image. Therefore, many researchers use eye detectors to obtain stable normalized face images. Because the eyes have salient patterns in the human face image, they can be located stably and used for face image normalization. The eye detection becomes more important when we want to apply model-based face image analysis approaches.

An Effective Method to Reduce False Alarms in Video Face Detection

2013 ◽  
Vol 347-350 ◽  
pp. 3619-3623
Author(s):  
Bing Li ◽  
Yuan Yan Tang ◽  
Di Wen ◽  
Zhen Chao Zhang ◽  
Bo Yang Ding
Keyword(s):  
Face Detection ◽  
Detection System ◽  
False Alarms ◽  
Fusion Method ◽  
Integral Image ◽  
Paper Briefly ◽  
Model Fusion ◽  
Adaboost Algorithm ◽  
Detector Structure ◽  
Face Detector

This paper briefly introduced the development of video face detection and point out the shortage of current face detection system that may produce much of false alarms. Then we detail the classic Viola face detector which using integral image, Haar-like features and AdaBoost algorithm for training. Compared with Viola face detector, we proposed an available multi-model fusion method to reduce false alarms in video face detection that is combining head-shoulder detector with HOG features. After introduced the related knowledge of HOG features, we proposed a fusion detector structure which can improve the accuracy and efficiency of detection.

Based on the Adaboost Algorithm of Face Detection Research

2014 ◽  
Vol 635-637 ◽  
pp. 985-988
Author(s):  
Wei Bo Yu ◽  
Lin Zhao ◽  
Wei Ming He
Keyword(s):  
Face Detection ◽  
Recognition System ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Adaboost Algorithm ◽  
Complex Background ◽  
Complex Image ◽  
Face Recognition System ◽  
Lower Accuracy ◽  
The Face

Because of the influence of complex image background, illumination changes, facial rotation and some other factors, makes face detection in complex background is much more difficult, lower accuracy and slower speed. Adaboost algorithm was used for face detection, and implemented the test process in OpenCV. Face detection experiments were performed on images with facial rotation and complex background, the detection accuracy rate was 85% and 99% respectively, the average detection time of each picture was 16.67ms and 76ms.Experimental results show that the face detection algorithm can accurately and quickly realize face detection in complex background, and can satisfy the requirements of real-time face recognition system.

Fast Face Detection Based on AdaBoost and Canny Operators

2014 ◽  
Vol 716-717 ◽  
pp. 936-939
Author(s):  
Lin Zhang
Keyword(s):  
Face Detection ◽  
Detection Method ◽  
Face Image ◽  
Experimental Results ◽  
Detection Accuracy ◽  
Human Face ◽  
Adaboost Algorithm ◽  
Amount Of Information ◽  
Detection Speed

Detection speed of traditional face detection method based on AdaBoost algorithm is slow since AdaBoost asks a large number of features. Therefore, to address this shortcoming, we proposed a fast face detection method based on AdaBoost and canny operators in this paper. Firstly, we use canny operators to detect edge of face image which separates the region of the possible human face from image, and then do face detection in the separated region using Modest AdaBoost algorithm (MAB). Before using MAB to achieve face detection, utilizing canny operators to detect edge can make this algorithm effectively filter information, retain useful information, reduce the amount of information and improve detection speed. Experimental results show that the algorithm can obtain higher detection accuracy and detection speed has been significantly improved at the same time.

scholarly journals Security Implementation by Machine Learning Based Face Recognization with SVM

2021 ◽  
pp. 214-218
Author(s):  
Apurva Yawalikar ◽  
U. W. Hore
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Face Detection ◽  
Detection System ◽  
Object Class ◽  
Detection Systems ◽  
The Face ◽  
Object Class Detection ◽  
Human Faces ◽  
Work Done

Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene. Face detection can be regarded as a specific case of object-class detection. In object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given. As per the various face detection system seen various work done onto the detection with various way. In existing this are get evaluate with the HOG with SVM, which will help us to get the exact value so that it is necessary to implement the system which will more effective and advance. As per the face detection seen there are various face detection systems are implemented. Determining face is easy but recognition is quite typical so that we are proposed machine learning based face recognition with SVM which helps to determine and detect the faces So the proposed system will get integrated with highly efficient and effective SVM model for face recognition. The proposed methodology will help us to implement the face based security implementation in any security system like door lock, mobile screen lock etc.

Multi-View Face Detection Based on Multi-Features AdaBoost Collaborative Learning Algorithm

2014 ◽  
Vol 998-999 ◽  
pp. 884-888
Author(s):  
Rong Bing Huang ◽  
Hong Zhang ◽  
Chang Ming Shu
Keyword(s):  
Collaborative Learning ◽  
Face Detection ◽  
Skin Color ◽  
Local Binary Pattern ◽  
Learning Algorithm ◽  
Standard Test ◽  
System Structure ◽  
Adaboost Algorithm ◽  
The Face ◽  
Candidate Regions

In View of the Multi-View Face Detection Problem under Complex Background, an Improved Face Detection Method Based on Multi-Features Boosting Collaborative Learning Algorithm Integrating Local Binary Pattern (LBP) is Presented. Firstly, Facial Skin Color Information is Used to Exclude most of the Background Regions. then, Haar-like Feature and LBP Feature are Extracted from Facial Candidate Regions and Inputted into a Modified Adaboost Algorithm to Obtain a Strong Classifier. Lastly, in Order to Improve the Detection Speed, Pyramid Classifier System Structure is Adopted to Determine the Face. the Experimental Results on CMU Standard Test Set and Life Photos, the Proposed Method has Achieved the Rapid Detection of Multi-View Face Image.

Research and Improvement on the Algorithm of Face Region Detection Based on Skin Color Model

2014 ◽  
Vol 543-547 ◽  
pp. 2702-2705
Author(s):  
Hong Hai Liu ◽  
Xiang Hua Hou
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Detection Algorithm ◽  
Face Image ◽  
Color Model ◽  
Region Detection ◽  
Face Region ◽  
The Face ◽  
Skin Color Model ◽  
Better Than

In face image with complex background, the CbCr skin color region will have offset when considering the illumination change. Therefore, the non-skin color pixels which luminance is less than 80 will be mistaken as skin color pixels and the skin color pixels which luminance is greater than 230 will be mistaken as non-skin color pixels. In order to reduce the misjudgments, an improved skin color model of nonlinear piecewise is put forward in this paper. Firstly, the skin color model of non-piecewise is analyzed and the experimental results show that by this model there is an obvious misjudgment in face detection. Then the skin color model of nonlinear piecewise is mainly analyzed and is demonstrated by mathematics method. A large number of training results show that the skin color model of nonlinear piecewise has better clustering distribution than the skin color model of non-piecewise. At lastly, the face detection algorithm adopting skin color model of nonlinear piecewise is analyzed. The results show that this algorithm is better than the algorithm adopting skin color model of non-piecewise and it makes a good foundation for the application of face image.

A Realization of Face Detection System Based on ARM Linux

2013 ◽  
Vol 333-335 ◽  
pp. 864-867 ◽  
Author(s):  
Cong Ting Zhao ◽  
Hong Yun Wang ◽  
Jia Wei Li ◽  
Zi Lu Ying
Keyword(s):  
Embedded System ◽  
Monitoring System ◽  
Face Detection ◽  
Detection System ◽  
Detection Algorithm ◽  
Video Monitoring ◽  
Adaboost Algorithm ◽  
Arm Processor ◽  
The Face ◽  
Detection Effect

In order to adapt to the requirements of intelligent video monitoring system, this paper presents an ARM-Linux based video monitoring system for face detection. In this system, an ARM processor with a Linux operating system was used, and the USB camera was used to capture data, and then the face detection was conducted in the ARM device. The OpenCV library was transplanted to Linux embedded system. The algorithm of face detection was realized by calling the OpenCV library. Specially, adaboost algorithm was chose as the face detection algorithm. Experimental results show that the face detection effect of the system is satisfactory and can meet the real time requirement of video surveillance.

Sign in / Sign up

Export Citation Format

Share Document