The Viola-Jones Face Detection Algorithm Analysis: A Survey

2021 ◽  
pp. 85-95
Author(s):  
Ahmed A. Elngar ◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Resource Allocation ◽  
Face Detection ◽  
Detection System ◽  
Detection Algorithm ◽  
Algorithm Analysis ◽  
Post Processing ◽  
Integral Image ◽  
Robust Detection ◽  
Processing Step ◽  
Haar Feature

In this paper, we analysis the Viola-Jones algorithm, the most real-time face detection system has been used. It is consisting from three main concepts to enable a robust detection: the integral image for Haar feature computation, Adaboost for selecting feature and cascade to make resource allocation more efficient. Here we propose each stage starting from Integral image to the end with Cascading and some of algorithmic description for stages. The Viola-Jones algorithm gives multiple detections, a post-processing step which reduce detection redundancy using Adaboost and cascading.

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.

Efficient Half Face Detection System Based on Linear Binary Pattern

2020 ◽  
Vol 17 (5) ◽  
pp. 2342-2348
Author(s):  
Ashutosh Upadhyay ◽  
S. Vijayalakshmi
Keyword(s):  
Face Detection ◽  
Detection System ◽  
False Positive Rate ◽  
Computational Cost ◽  
Detection Algorithm ◽  
Data Set ◽  
Computation Cost ◽  
Face Features ◽  
Positive Rate ◽  
The Face

In the field of computer vision, face detection algorithms achieved accuracy to a great extent, but for the real time applications it remains a challenge to maintain the balance between the accuracy and efficiency i.e., to gain accuracy computational cost also increases to deal with the large data sets. This paper, propose half face detection algorithm to address the efficiency of the face detection algorithm. The full face detection algorithm consider complete face data set for training which incur more computation cost. To reduce the computation cost, proposed model captures the features of the half of the face by assuming that the human face is symmetric about the vertical axis passing through the nose and train the system using reduced half face features. The proposed algorithm extracts Linear Binary Pattern (LBP) features and train model using adaboost classifier. Algorithm performance is presented in terms of the accuracy i.e., True Positive Rate (TPR), False Positive Rate (FTR) and face recognition time complexity.

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%.

Defect Detection Study of Fabric Based on Gabor Filter Algorithm

2011 ◽  
Vol 697-698 ◽  
pp. 491-494
Author(s):  
G.X. Li ◽  
Y.F. Li
Keyword(s):  
Iterative Algorithm ◽  
Defect Detection ◽  
Gabor Filter ◽  
Detection Algorithm ◽  
Algorithm Analysis ◽  
Post Processing ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Multichannel Data ◽  
Defect Image

This thesis exploits a multichannel Gabor filters detection algorithm. Analysis filtering images from different orientations and scales, then fuses the multichannel data. Finally, a threshold iterative algorithm and mathematical morphology post-processing is used to achieve the fabric defect detection. The experiment selects five types of fabric defect image. Experimental results suggest that this algorithm can effectively identify blob-shaped, linear and planar defect and has well real-time character.

Fast Pedestrian Detection for Intelligent Vehicle Based on FPGA and Monocular Vision

2013 ◽  
Vol 651 ◽  
pp. 784-788
Author(s):  
Xiao Dong Miao ◽  
Shun Ming Li ◽  
Min Xiang Wei ◽  
Huan Shen
Keyword(s):  
Detection System ◽  
Pedestrian Detection ◽  
Detection Algorithm ◽  
Intelligent Vehicle ◽  
Image Generation ◽  
Integral Image ◽  
Verilog Hdl ◽  
Adaboost Algorithm ◽  
Image Scaling ◽  
Computational Speed

This paper presents a fast pedestrian detection algorithm for intelligent vehicle based on FPGA architecture, using AdaBoost algorithm and Haar features. We describe the hardware design including image scaling, integral image generation, pipelined processing as well as classifier, and parallel processing multiple classifiers to accelerate the computational speed of the pedestrian detection system. The proposed architecture for pedestrian detection has been tested using Verilog HDL and implemented in Xilinx Virtex-5 FPGA. Its performance has been measured about 38 times than the equivalent software implementation.

Optimized Parallel Implementation of Face Detection Based on Embedded Heterogeneous Many-Core Architecture

2017 ◽  
Vol 31 (07) ◽  
pp. 1756011 ◽  
Author(s):  
Fang Gao ◽  
Zhangqin Huang ◽  
Shulong Wang ◽  
Xinrong Ji
Keyword(s):  
High Resolution ◽  
Face Detection ◽  
Parallel Implementation ◽  
Detection System ◽  
Local Binary Patterns ◽  
Detection Algorithm ◽  
Computing Platform ◽  
Computing Performance ◽  
Many Core ◽  
Dual Core

Computing performance is one of the key problems in embedded systems for high-resolution face detection applications. To improve the computing performance of embedded high-resolution face detection systems, a novel parallel implementation of embedded face detection system was established based on a low power CPU-Accelerator heterogeneous many-core architecture. First, a basic CPU version of face detection prototype was implemented based on the cascade classifier and Local Binary Patterns operator. Second, the prototype was extended to a specified embedded parallel computing platform that is called Parallella and consists of Xilinx Zynq and Adapteva Epiphany. Third, the face detection algorithm was optimized to adapt to the Parallella architecture to improve the detection speed and the utilization of computing resources. Finally, a face detection experiment was conducted to evaluate the computing performance of the proposal in this paper. The experimental results show that the proposed implementation obtained a very consistent accuracy as that of the dual-core ARM, and achieved 7.8 times speedup than that of the dual-core ARM. Experiment results prove that the proposed implementation has significant advantages on computing performance.

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.

scholarly journals Face Detection and Processing: a Survey

2018 ◽  
Vol 7 (4.19) ◽  
pp. 1066
Author(s):  
R. P.Dahake ◽  
M. U. Kharat
Keyword(s):  
Real Time ◽  
Face Detection ◽  
Detection System ◽  
Research Work ◽  
Processing Unit ◽  
Detection Systems ◽  
Haar Feature ◽  
The Face ◽  
Feature Based ◽  
Graphical Processing

In the recent era facial image processing is gaining more importance and the face detection from image or from video have  number of applications  which are video surveillance, entertainment, security, multimedia, communication, Ubiquitous computing etc. Various research work are carried out for  face detection and processing which includes detection, tracking of the face, estimation of pose, clustering the detected faces etc. Although significant advances have been made, the performance of face detection systems provide satisfactory under controlled environment & may get degraded with some challenging scenario such as in real time video face detection and processing. There are many real-time applications where human face serves as identity and these application are time bound so time for detection of face from image or video and the further processing is very essential, thus here our goal is to discuss the face detection system overview and to review various human skin colors based approaches and Haar feature based approach for better detection performance. Detected faces tagging and clustering is essential in some cases, so for such further processing time factor plays important role. Some of the recent approaches to improve detection speed such as using Graphical Processing Unit are discussed and providing future directions in this area. 

scholarly journals Face Tracking for Flying Robot Quadcopter based on Haar Cascade Classifier and PID Controller

2021 ◽  
Vol 2111 (1) ◽  
pp. 012046
Author(s):  
A S Priambodo ◽  
F Arifin ◽  
A Nasuha ◽  
A Winursito
Keyword(s):  
Programming Languages ◽  
Face Detection ◽  
Position Control ◽  
Detection System ◽  
Response Speed ◽  
Detection Algorithm ◽  
The Face ◽  
Haar Cascade ◽  
Cascade Method ◽  
The Right

Abstract The fundamental aim of this research is to develop a face detection system for a quadcopter in order to follow the face object. This research has two main stages, namely the face detection stage and the position control system. The face detection algorithm used in this research is the haar cascade method which is run using the python and OpenCV programming languages. The algorithm worked well, getting around 16fps on a low spec computer without a GPU unit. The results of the face detection algorithm are proven to be able to recognize faces from the camera installed on the DJI Tello mini drone. The mini drone was chosen because it is small and light, so it is harmless, and testing can be carried out indoors. Besides, the DJI Tello can be programmed easily using the python programming language. The drone’s position is then compared with the set point in the middle of the image to obtain errors so that control signals can be calculated for up/down, forward/backward, and right/left movements. From the testing results, the response speed that occurs in the right/left and up/down movements is less than 2 seconds, while for the forward/backward movement, it is less than 3 seconds.

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