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 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 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.

scholarly journals Host-based Intrusion Detection System (HIDS)

2020 ◽  
Vol 9 (5) ◽  
pp. 1043-1049
Keyword(s):  
Intrusion Detection System ◽  
Detection System ◽  
Data Extraction ◽  
False Positive Rate ◽  
Denial Of Service ◽  
Machine Learning Algorithms ◽  
Service Network ◽  
Data Set ◽  
Positive Rate ◽  
The Individual

This paper presents the data analysis and feature extraction of KDD dataset of 1999. This is used to detect signature based and anomaly attacks on a system. The process is supported by data extraction as well as data cleaning of the above mentioned data set. The dataset consists of 42 parameters and 58 services. These parameters are further filtered to extract useful attributes. Every attack in the dataset is labeled either with “normal” or into four different attack types i.e. denial-of-service, network probe, remote-to-local or user-to-root. Using different machine learning algorithms, the work tries to compare the individual accuracy, True Positive and False positive rate of every algorithm with every other algorithm. The work focuses its attention to increase security through detection of static as well as dynamic attack.

scholarly journals IoT Botnet Anomaly Detection Using Unsupervised Deep Learning

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1876
Author(s):  
Ioana Apostol ◽  
Marius Preda ◽  
Constantin Nila ◽  
Ion Bica
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
False Positive Rate ◽  
Empirical Evaluation ◽  
Learning Approaches ◽  
Promising Alternative ◽  
Positive Rate ◽  
Unsupervised Deep Learning ◽  
Attack Surface ◽  
Iot Devices

The Internet of Things has become a cutting-edge technology that is continuously evolving in size, connectivity, and applicability. This ecosystem makes its presence felt in every aspect of our lives, along with all other emerging technologies. Unfortunately, despite the significant benefits brought by the IoT, the increased attack surface built upon it has become more critical than ever. Devices have limited resources and are not typically created with security features. Lately, a trend of botnet threats transitioning to the IoT environment has been observed, and an army of infected IoT devices can expand quickly and be used for effective attacks. Therefore, identifying proper solutions for securing IoT systems is currently an important and challenging research topic. Machine learning-based approaches are a promising alternative, allowing the identification of abnormal behaviors and the detection of attacks. This paper proposes an anomaly-based detection solution that uses unsupervised deep learning techniques to identify IoT botnet activities. An empirical evaluation of the proposed method is conducted on both balanced and unbalanced datasets to assess its threat detection capability. False-positive rate reduction and its impact on the detection system are also analyzed. Furthermore, a comparison with other unsupervised learning approaches is included. The experimental results reveal the performance of the proposed detection method.

Investigating the clinical usefulness of definitions of progression with 10-2 visual field

2021 ◽  
pp. bjophthalmol-2020-318188
Author(s):  
Shotaro Asano ◽  
Hiroshi Murata ◽  
Yuri Fujino ◽  
Takehiro Yamashita ◽  
Atsuya Miki ◽  
...  
Keyword(s):  
Visual Field ◽  
False Positive ◽  
False Positive Rate ◽  
Clinical Validity ◽  
Data Set ◽  
Humphrey Field Analyzer ◽  
Positive Rate ◽  
Using Data ◽  
Sensitivity Specificity ◽  
Higher Sensitivity

Background/AimTo investigate the clinical validity of the Guided Progression Analysis definition (GPAD) and cluster-based definition (CBD) with the Humphrey Field Analyzer 10-2 test in diagnosing glaucomatous visual field (VF) progression, and to introduce a novel definition with optimised specificity by combining the ‘any-location’ and ‘cluster-based’ approaches (hybrid definition).Methods64 400 stable glaucomatous VFs were simulated from 664 pairs of 10-2 tests (10 sets × 10 VF series × 664 eyes; data set 1). Using these simulated VFs, the specificity to detect progression and the effects of changing the parameters (number of test locations or consecutive VF tests, and percentile cut-off values) were investigated. The hybrid definition was designed as the combination where the specificity was closest to 95.0%. Subsequently, another 5000 actual glaucomatous 10-2 tests from 500 eyes (10 VFs each) were collected (data set 2), and their accuracy (sensitivity, specificity and false positive rate) and the time needed to detect VF progression were evaluated.ResultsThe specificity values calculated using data set 1 with GPAD and CBD were 99.6% and 99.8%. Using data set 2, the hybrid definition had a higher sensitivity than GPAD and CBD, without detriment to the specificity or false positive rate. The hybrid definition also detected progression significantly earlier than GPAD and CBD (at 3.1 years vs 4.2 years and 4.1 years, respectively).ConclusionsGPAD and CBD had specificities of 99.6% and 99.8%, respectively. A novel hybrid definition (with a specificity of 95.5%) had higher sensitivity and enabled earlier detection of progression.

scholarly journals Hybrid Internal Anomaly Detection System for IoT: Reactive Nodes with Cross-Layer Operation

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Nanda Kumar Thanigaivelan ◽  
Ethiopia Nigussie ◽  
Seppo Virtanen ◽  
Jouni Isoaho
Keyword(s):  
Anomaly Detection ◽  
False Positive ◽  
Detection System ◽  
False Positive Rate ◽  
Security Analysis ◽  
Abnormal Behavior ◽  
Test Bed ◽  
Positive Rate ◽  
Parent Node ◽  
Anomaly Detection System

We present a hybrid internal anomaly detection system that shares detection tasks between router and nodes. It allows nodes to react instinctively against the anomaly node by enforcing temporary communication ban on it. Each node monitors its own neighbors and if abnormal behavior is detected, the node blocks the packets of the anomaly node at link layer and reports the incident to its parent node. A novel RPL control message, Distress Propagation Object (DPO), is formulated and used for reporting the anomaly and network activities to the parent node and subsequently to the router. The system has configurable profile settings and is able to learn and differentiate between the nodes normal and suspicious activities without a need for prior knowledge. It has different subsystems and operation phases that are distributed in both the nodes and router, which act on data link and network layers. The system uses network fingerprinting to be aware of changes in network topology and approximate threat locations without any assistance from a positioning subsystem. The developed system was evaluated using test-bed consisting of Zolertia nodes and in-house developed PandaBoard based gateway as well as emulation environment of Cooja. The evaluation revealed that the system has low energy consumption overhead and fast response. The system occupies 3.3 KB of ROM and 0.86 KB of RAM for its operations. Security analysis confirms nodes reaction against abnormal nodes and successful detection of packet flooding, selective forwarding, and clone attacks. The system’s false positive rate evaluation demonstrates that the proposed system exhibited 5% to 10% lower false positive rate compared to simple detection system.

Research and Implementation of Face Detection Based on OpenCV

2014 ◽  
Vol 971-973 ◽  
pp. 1710-1713
Author(s):  
Wen Huan Wu ◽  
Ying Jun Zhao ◽  
Yong Fei Che
Keyword(s):  
Face Recognition ◽  
Detailed Analysis ◽  
Real Time ◽  
Face Detection ◽  
Recognition System ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Accuracy Rate ◽  
Face Recognition System ◽  
The Face

Face detection is the key point in automatic face recognition system. This paper introduces the face detection algorithm with a cascade of Adaboost classifiers and how to configure OpenCV in MCVS. Using OpenCV realized the face detection. And a detailed analysis of the face detection results is presented. Through experiment, we found that the method used in this article has a high accuracy rate and better real-time.

scholarly journals Embedded Smart Car Security System on Face Detection

2014 ◽  
pp. 176-180
Author(s):  
Vikram Kulkarni ◽  
Viswaprakash Babu
Keyword(s):  
Face Detection ◽  
Detection System ◽  
Security System ◽  
Experimental Results ◽  
Embedded Platform ◽  
Web Camera ◽  
The Face ◽  
Car Security ◽  
System Prototype

In this proposed embedded car security system, FDS(Face Detection System) is used to detect the face of the driver and compare it with the predefined face. For example, in the night when the car’s owner is sleeping and someone theft the car then FDS obtains images by one tiny web camera which can be hidden easily in somewhere in the car. FDS compares the obtained image with the predefined images if the image doesn’t match, then the information is sent to the owner through MMS. So now owner can obtain the image of the thief in his mobile as well as he can trace the location through GPS. The location of the car as well as its speed can be displayed to the owner through SMS. So by using this system, owner can identify the thief image as well as the location of the car This system prototype is built on the base of one embedded platform in which one SoC named “SEP4020”(works at 100MHz) controls all the processes .Experimental results illuminate the validity of this car security system.

scholarly journals Mixture models reveal multiple positional bias types in RNA-Seq data and lead to accurate transcript concentration estimates

2014 ◽  
Author(s):  
Andreas Tuerk ◽  
Gregor Wiktorin ◽  
Serhat Güler
Keyword(s):  
Probability Distributions ◽  
False Positive Rate ◽  
Synthetic Data ◽  
True Positive Rate ◽  
Rna Seq ◽  
Microarray Quality Control ◽  
Data Set ◽  
Rna Transcripts ◽  
Positive Rate ◽  
Fragment Distribution

Quantification of RNA transcripts with RNA-Seq is inaccurate due to positional fragment bias, which is not represented appropriately by current statistical models of RNA-Seq data. This article introduces the Mix2(rd. "mixquare") model, which uses a mixture of probability distributions to model the transcript specific positional fragment bias. The parameters of the Mix2model can be efficiently trained with the Expectation Maximization (EM) algorithm resulting in simultaneous estimates of the transcript abundances and transcript specific positional biases. Experiments are conducted on synthetic data and the Universal Human Reference (UHR) and Brain (HBR) sample from the Microarray quality control (MAQC) data set. Comparing the correlation between qPCR and FPKM values to state-of-the-art methods Cufflinks and PennSeq we obtain an increase in R2value from 0.44 to 0.6 and from 0.34 to 0.54. In the detection of differential expression between UHR and HBR the true positive rate increases from 0.44 to 0.71 at a false positive rate of 0.1. Finally, the Mix2model is used to investigate biases present in the MAQC data. This reveals 5 dominant biases which deviate from the common assumption of a uniform fragment distribution. The Mix2software is available at http://www.lexogen.com/fileadmin/uploads/bioinfo/mix2model.tgz.

scholarly journals ScoreNet: A neural network-based post-processing model for identifying epileptic seizure onset and offset in EEGs

2020 ◽  
Author(s):  
Poomipat Boonyakitanont ◽  
Apiwat Lek-uthai ◽  
Jitkomut Songsiri
Keyword(s):  
Neural Network ◽  
Epileptic Seizure ◽  
False Positive Rate ◽  
Imbalanced Data ◽  
Detection Algorithm ◽  
Processing Technique ◽  
Post Processing ◽  
Scalp Eeg ◽  
Positive Rate ◽  
Eeg Recordings

AbstractThis article aims to design an automatic detection algorithm of epileptic seizure onsets and offsets in scalp EEGs. A proposed scheme consists of two sequential steps: the detection of seizure episodes, and the determination of seizure onsets and offsets in long EEG recordings. We introduce a neural network-based model called ScoreNet as a post-processing technique to determine the seizure onsets and offsets in EEGs. A cost function called a log-dice loss that has an analogous meaning to F1 is proposed to handle an imbalanced data problem. In combination with several classifiers including random forest, CNN, and logistic regression, the ScoreNet is then verified on the CHB-MIT Scalp EEG database. As a result, in seizure detection, the ScoreNet can significantly improve F1 to 70.15% and can considerably reduce false positive rate per hour to 0.05 on average. In addition, we propose detection delay metric, an effective latency index as a summation of the exponential of delays, that includes undetected events into account. The index can provide a better insight into onset and offset detection than conventional time-based metrics.

Sign in / Sign up

Export Citation Format

Share Document