Face Detection System Based on AdaBoost Algorithm

2013 ◽  
Vol 380-384 ◽  
pp. 3917-3920
Author(s):  
Lan Shi ◽  
Jian Hui Lv
Keyword(s):  
Face Recognition ◽  
Work Environment ◽  
Detection Rate ◽  
Detection System ◽  
Small Range ◽  
System Software ◽  
Hardware Platform ◽  
Adaboost Algorithm ◽  
Simulation Results ◽  
Detection Speed

In order to do further research on face recognition, this paper constructs system software work environment on the hardware platform, and then AdaBoost algorithm is given and transplanted into this system. According to the detection speed of the system and the detection rate, this paper does simulation results, it shows that the speed of each frame image detected by the system is about 110 to 130 milliseconds, and the detection rate of face rotation of small range is 85% or more, which shows the system can meet the practical needs and has widely application.

Illumination Variant Face Detection System Using Hierarchical Feature Method

2015 ◽  
Vol 764-765 ◽  
pp. 1309-1313
Author(s):  
Chih Hsien Hsia ◽  
Jen Shiun Chiang ◽  
Chin Yi Lin
Keyword(s):  
Detection Rate ◽  
Detection System ◽  
Back Propagation ◽  
Texture Features ◽  
Global Features ◽  
Adaboost Algorithm ◽  
Local And Global Features ◽  
Propagation Network ◽  
Human Faces ◽  
Feature Calculation

In this study, we propose a new solution based on Adaboost algorithm and Back Propagation Network (BPN) of Neural Network (NN) combining local and global features with cascade architecture to detect human faces. We use Modified Census Transform (MCT) feature that belong to texture features and is less sensitive to illumination for local feature calculation. By this approach, it is not necessary to preprocess each sub-window of the image. For classification, we use the structure of hierarchical feature to control the number of features. With only MCT, it is easy to misjudge faces, and therefore in this work we include the brightness information of global features to eliminate the false positive regions. As a result, the proposed approach can have Detection Rate (DR) of 99%, false positives of only 11, and detection speed of 27.92 Frame Per Second (FPS).

LSO-AdaBoost Based Face Detection for IP-CAM Video

2013 ◽  
Vol 284-287 ◽  
pp. 3543-3548 ◽  
Author(s):  
Chuang Jan Chang ◽  
Shu Lin Hwang
Keyword(s):  
Video Surveillance ◽  
Face Detection ◽  
Motion Detection ◽  
Detection Rate ◽  
Detection System ◽  
Training Data ◽  
Surveillance Systems ◽  
High Detection Rate ◽  
Data Set ◽  
Adaboost Algorithm

The IP-CAM plays a major role in the context of digital video surveillance systems. The function of face detection can add extra value and can contribute towards an intelligent video surveillance system. The cascaded AdaBoost-based face detection system proposed by Viola can support real-time detection with a high detection rate. The performance of the Alt2 cascade (from OpenCV) in an IP-CAM video is worse than that with regard to static images because the training data set in the Alt2 cannot consider the localized characters in the special IP-CAM video. Therefore, this study presents an enhanced training method using the Adaboost algorithm which is capable of obtaining the localized sampling optimum (LSO) from a local IP-CAM video. In addition, we use an improved motion detection algorithm that cooperates with the former face detector to speed up processing time and achieve a better detection rate on video-rate processing speed. The proposed solution has been developed around the cascaded AdaBoost approach, using the open-CV library, with a LSO from a local IP-CAM video. An efficient motion detection model is adopted for practical applications. The overall system performance using 30% local samples can be improved to a 97.9% detection rate and reduce detection time by 54.5% with regard to the Alt2 cascade.

scholarly journals Body Parts Features-Based Pedestrian Detection for Active Pedestrian Protection System

2016 ◽  
Vol 28 (2) ◽  
pp. 133-142 ◽  
Author(s):  
Lie Guo ◽  
Mingheng Zhang ◽  
Linhui Li ◽  
Yibing Zhao ◽  
Yingzi Lin
Keyword(s):  
Detection Rate ◽  
Detection Method ◽  
Detection System ◽  
Pedestrian Detection ◽  
Urban Traffic ◽  
Body Parts ◽  
Detection Time ◽  
Pedestrian Protection ◽  
Adaboost Algorithm ◽  
Threshold Adjustment

A novel pedestrian detection system based on vision in urban traffic situations is presented to help the driver perceive the pedestrian ahead of the vehicle. To enhance the accuracy and to decrease the time spent on pedestrian detection in such complicated situations, the pedestrian is detected by dividing their body into several parts according to their corresponding features in the image. The candidate pedestrian leg is segmented based on the gentle AdaBoost algorithm by training the optimized histogram of gradient features. The candidate pedestrian head is located by matching the pedestrian head and shoulder model above the region of the candidate leg. Then the candidate leg, head and shoulder are combined by parts constraint and threshold adjustment to verify the existence of the pedestrian. Finally, the experiments in real urban traffic circumstances were conducted. The results show that the proposed pedestrian detection method can achieve pedestrian detection rate of 92.1% with the average detection time of 0.2257 s.

scholarly journals Design of HIL for Multilevel Inverter Using Zynq-7000 Platform – Part 2

2017 ◽  
Vol 61 (3) ◽  
pp. 272
Author(s):  
Balázs Farkas ◽  
Károly Veszprémi
Keyword(s):  
Resource Utilization ◽  
Software Design ◽  
Model Transformation ◽  
Electronic Devices ◽  
Multilevel Inverter ◽  
Power Electronic ◽  
Hardware In The Loop ◽  
System Software ◽  
Hardware Platform ◽  
Simulation Results

Development of power electronic devices requires multi -disciplined engineering activities. These cover the thermal, electrical and software design. Due to this design complexity rapid prototyping methods and model-based design are becoming more and more important in the R&D projects in this field. This article is the second part of the series which introduces the development of Hardware-in-the-Loop (HIL) device for the simulation of Cellular H-Bridge inverter (CHB). Zynq-7000 platform is chosen as a hardware platform for HIL. This part focuses on the details of the model transformation, development of the hardware environment and the verification of the HIL. FPGA development is also demonstrated including interfaces, IPs and introduction of the resource utilization. Apart from them, operation of the system software in ARM core is also described including TCP/IP interface, IRQ handling and Matlab synchronisation mechanism. Finally, the Matlab interface and simulation results are introduced.

AdaBoost Algorithm with Single Weak Classifier in Network Intrusion Detection

2016 ◽  
pp. 259-269 ◽  
Author(s):  
P. Natesan ◽  
P. Balasubramanie ◽  
G. Gowrison
Keyword(s):  
Intrusion Detection ◽  
Classification Accuracy ◽  
Intrusion Detection System ◽  
Detection Rate ◽  
Detection System ◽  
Network Intrusion Detection ◽  
Weak Classifier ◽  
Adaboost Algorithm ◽  
Network Intrusion ◽  
Boosting Algorithm

Recently machine learning based intrusion detection system developments have been subjected to extensive researches because they can detect both misuse detection and anomaly detection. In this paper, we propose an AdaBoost based algorithm for network intrusion detection system with single weak classifier. In this algorithm, the classifiers such as Bayes Net, Naïve Bayes and Decision tree are used as weak classifiers. KDDCup99 dataset is used in these experiments to demonstrate that boosting algorithm can greatly improve the classification accuracy of weak classification algorithms. Our approach achieves higher detection rate with low false alarm rates and is scalable for large datasets, resulting in an effective intrusion detection system.

scholarly journals Application of portrait recognition system for emergency evacuation in mass emergencies

2021 ◽  
Vol 30 (1) ◽  
pp. 893-902
Author(s):  
Ke Xu
Keyword(s):  
Detection Rate ◽  
Characteristic Curve ◽  
Recognition Rate ◽  
Recognition System ◽  
Emergency Evacuation ◽  
Shopping Mall ◽  
Single Shot ◽  
Data Set ◽  
Linear Discriminant ◽  
Adaboost Algorithm

Abstract A portrait recognition system can play an important role in emergency evacuation in mass emergencies. This paper designed a portrait recognition system, analyzed the overall structure of the system and the method of image preprocessing, and used the Single Shot MultiBox Detector (SSD) algorithm for portrait detection. It also designed an improved algorithm combining principal component analysis (PCA) with linear discriminant analysis (LDA) for portrait recognition and tested the system by applying it in a shopping mall to collect and monitor the portrait and establish a data set. The results showed that the missing detection rate and false detection rate of the SSD algorithm were 0.78 and 2.89%, respectively, which were lower than those of the AdaBoost algorithm. Comparisons with PCA, LDA, and PCA + LDA algorithms demonstrated that the recognition rate of the improved PCA + LDA algorithm was the highest, which was 95.8%, the area under the receiver operating characteristic curve was the largest, and the recognition time was the shortest, which was 465 ms. The experimental results show that the improved PCA + LDA algorithm is reliable in portrait recognition and can be used for emergency evacuation in mass emergencies.

Face recognition in an intelligent door lock with ResNet model based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Suphawimon Phawinee ◽  
Jing-Fang Cai ◽  
Zhe-Yu Guo ◽  
Hao-Ze Zheng ◽  
Guan-Chen Chen
Keyword(s):  
Face Recognition ◽  
Detection System ◽  
Low Cost ◽  
Ease Of Use ◽  
Raspberry Pi ◽  
Training Methods ◽  
Graphics Recognition ◽  
Face Images ◽  
The Face ◽  
Door Lock

Internet of Things is considerably increasing the levels of convenience at homes. The smart door lock is an entry product for smart homes. This work used Raspberry Pi, because of its low cost, as the main control board to apply face recognition technology to a door lock. The installation of the control sensing module with the GPIO expansion function of Raspberry Pi also improved the antitheft mechanism of the door lock. For ease of use, a mobile application (hereafter, app) was developed for users to upload their face images for processing. The app sends the images to Firebase and then the program downloads the images and captures the face as a training set. The face detection system was designed on the basis of machine learning and equipped with a Haar built-in OpenCV graphics recognition program. The system used four training methods: convolutional neural network, VGG-16, VGG-19, and ResNet50. After the training process, the program could recognize the user’s face to open the door lock. A prototype was constructed that could control the door lock and the antitheft system and stream real-time images from the camera to the app.

Faster multi-defect detection system in shield tunnel using combination of FCN and faster RCNN

2019 ◽  
Vol 22 (13) ◽  
pp. 2907-2921 ◽  
Author(s):  
Xinwen Gao ◽  
Ming Jian ◽  
Min Hu ◽  
Mohan Tanniru ◽  
Shuaiqing Li
Keyword(s):  
Boundary Layer ◽  
Defect Detection ◽  
Detection Rate ◽  
Layer Structure ◽  
Detection System ◽  
Identification Accuracy ◽  
Shield Tunnel ◽  
False Detection Rate ◽  
False Detection ◽  
Data Set

With the large-scale construction of urban subways, the detection of tunnel defects becomes particularly important. Due to the complexity of tunnel environment, it is difficult for traditional tunnel defect detection algorithms to detect such defects quickly and accurately. This article presents a deep learning FCN-RCNN model that can detect multiple tunnel defects quickly and accurately. The algorithm uses a Faster RCNN algorithm, Adaptive Border ROI boundary layer and a three-layer structure of the FCN algorithm. The Adaptive Border ROI boundary layer is used to reduce data set redundancy and difficulties in identifying interference during data set creation. The algorithm is compared with single FCN algorithm with no Adaptive Border ROI for different defect types. The results show that our defect detection algorithm not only addresses interference due to segment patching, pipeline smears and obstruction but also the false detection rate decreases from 0.371, 0.285, 0.307 to 0.0502, respectively. Finally, corrected by cylindrical projection model, the false detection rate is further reduced from 0.0502 to 0.0190 and the identification accuracy of water leakage defects is improved.

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.

SIMULATION AND MODELING OF EVOKED RESPONSE ELECTROENCEPHALOGRAPH SIGNAL

1999 ◽  
Vol 10 (04) ◽  
pp. 759-776
Author(s):  
D. R. KULKARNI ◽  
J. C. PARIKH ◽  
R. PRATAP
Keyword(s):  
Small Range ◽  
Eeg Signal ◽  
Simulation And Modeling ◽  
State Space Reconstruction ◽  
Eyes Closed ◽  
Normal Individuals ◽  
Eeg Data ◽  
Closed Data ◽  
Simulation Results ◽  
Low Dimensional

Electroencephalograph (EEG) data for normal individuals with eyes-closed and under stimuli is analyzed. The stimuli consisted of photo, audio, motor and mental activity. We use several measures from nonlinear dynamics to analyze and characterize the data. We find that the dynamics of the EEG signal is deterministic and chaotic but it is not a low dimensional chaotic system. The evoked responses lead to a redistribution of strengths relative to eyes-closed data. Basically, strength in α waves decreases whereas that in β wave increases. We also carried out simulations separately and in combination for δ, θ, α and β waves to understand the data. From the simulation results, it appears that the characteristics of EEG data are consequences of filtering the data with a relatively small range of frequency (0.5–32 Hz). In view of this, we believe that calculation of known nonlinear measures is not likely to be very useful for studying the dynamics of EEG data. We have also successfully modeled the EEG time series using the concept of state space reconstruction in the framework of artificial neural network. It gives us confidence that one would be able to understand, in a more basic way, how collectivity in EEG signal arises.

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