scholarly journals Deep Neural Network for Better Face Processing

2019 ◽  
Vol 8 (4) ◽  
pp. 2236-2239
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Face Detection ◽  
Deep Neural Network ◽  
Low Cost ◽  
Raspberry Pi ◽  
The Face ◽  
Frame Work ◽  
Haar Cascade ◽  
Cascade Method

This Paper represents the face detection using advanced method deep neural network which uses deep learning frame work. The old models used to detect the faces were like Haar-cascade method which detect the faces with good approaches but there is some uncertainty in the accuracy of the old models, so in this system we will use the latest deep neural network model which is embedded with latest open cv and by using the deep learning model frame work which is weighted with some other files. By using this model, we can achieve the better accuracy in face detection which can be used for further purposes like auto focus in cameras, counting number of people etc. This model detects the faces accurately and paves the way for better recognition systems which can be used in many face biometric applications. For this purpose, low-cost computer board Raspberry Pi and Camera Sensor will be used.

scholarly journals Deep Learning-Based Portable Device for Audio Distress Signal Recognition in Urban Areas

2020 ◽  
Vol 10 (21) ◽  
pp. 7448
Author(s):  
Jorge Felipe Gaviria ◽  
Alejandra Escalante-Perez ◽  
Juan Camilo Castiblanco ◽  
Nicolas Vergara ◽  
Valentina Parra-Garces ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Urban Areas ◽  
Low Cost ◽  
Raspberry Pi ◽  
Automatic Identification ◽  
Signal Recognition ◽  
Main Challenge

Real-time automatic identification of audio distress signals in urban areas is a task that in a smart city can improve response times in emergency alert systems. The main challenge in this problem lies in finding a model that is able to accurately recognize these type of signals in the presence of background noise and allows for real-time processing. In this paper, we present the design of a portable and low-cost device for accurate audio distress signal recognition in real urban scenarios based on deep learning models. As real audio distress recordings in urban areas have not been collected and made publicly available so far, we first constructed a database where audios were recorded in urban areas using a low-cost microphone. Using this database, we trained a deep multi-headed 2D convolutional neural network that processed temporal and frequency features to accurately recognize audio distress signals in noisy environments with a significant performance improvement to other methods from the literature. Then, we deployed and assessed the trained convolutional neural network model on a Raspberry Pi that, along with the low-cost microphone, constituted a device for accurate real-time audio recognition. Source code and database are publicly available.

scholarly journals A Deep Neural Network for face Recognition

2019 ◽  
Vol 8 (12S) ◽  
pp. 420-423
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Face Recognition ◽  
Deep Neural Network ◽  
Facial Features ◽  
Biometric Authentication ◽  
Accuracy Rate ◽  
Learning Technique ◽  
The Face ◽  
Deep Learning Model

Face recognition is used to biometric authentication method to analyze the face extract and photographs useful to reputation formation from them, which can be usually called as a characteristic vector this is used to differentiate the organic features. In this paper to detect the suspect by extracting facial features from the captured image of the suspect from CCTV and match it with the pictures stored in the database and also to achieve an accuracy rate of 100 %, negligible loss using deep learning technique. For extracting the facial features, we are using deep learning model known as Convolutional Neural Network (CNN). It is one of the best models to extract features with the highest accuracy rate .

scholarly journals Smart attendance system using Convolution Neural Network and Image Processing

2020 ◽  
Vol 5 (5) ◽  
pp. 611-616
Author(s):  
Senigala Kuruba ChayaDevi ◽  
Vamsi Agnihotram
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Image Recognition ◽  
Imaging System ◽  
System Quality ◽  
Ann Model ◽  
Driver Assistance Systems ◽  
The Face ◽  
Haar Cascade ◽  
Cascade Method

Smart attendance maintenancesystem has been a research topic from past a fewdecades; each method has its own disadvantages and advantages.An algorithm using Convolutional Neural Network and Image processinghas been proposed in this paper to overcome the disadvantages of the previous algorithms. Image recognition is playing an important role in the modern living like driver assistance systems, medical imaging system, quality control system to name a few. An Artificial Neural Network along with image recognition used to enhance the reliability of the attendancesystem. One such update used here is CNN.Deep learning has been an emerging technology hence opted to implement the smart attendance system.The implementation basically consists of three components : 1)Face scanning and detection using HAAR cascade method 2)Training the CNN-ANN model 3)Recognize the face  and update the attendance .The main motivation of our work is to merge three of the emerging technologies : Machine learning , Image Processing and IOT . Key advantage of this implementation is that a deep learning model increases its accuracy with more epochs of training andit optimizes the run time.

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 A Simple Yet Reliable Facial Emotion Detection for Campus Environment

2019 ◽  
Vol 8 (3) ◽  
pp. 2477-2481
Keyword(s):  
Face Detection ◽  
Detection System ◽  
Confidence Score ◽  
Facial Emotion ◽  
Raspberry Pi ◽  
Face Alignment ◽  
Emotion Detection ◽  
Campus Environment ◽  
The Face ◽  
Haar Cascade

Nowadays, crime incidents like stealing, fighting and harassment often occur in campus leading to serious consequences. Students do not feel secure to study in campus anymore. Thus, a simple facial emotion detection system using a Raspberry Pi is introduced to help mitigating the issue before getting worse in campus. Two algorithms are used for this project including Haar Cascade and Local Binary Pattern (LBP) algorithms. OpenCV is a library that can be used for image processing. LBP algorithm is used for face detection in OpenCV. When a person enters the specified area, the camera will capture the image and detect the image of the person. Then, a rectangular box appears on the face image of the person. The image is automatically sent to the email. The face detection is enhanced by adding a face alignment. The face alignment is used to detect the location of many points on the face. It recognizes the emotions for each face and gives the confidence score. The value 0 of confidence score is the perfect face recognition. Although the system is simple, it is still reliable to be used in a campus environment.

scholarly journals Robust Approach to Supervised Deep Neural Network Training for Real-Time Object Classification in Cluttered Indoor Environment

2021 ◽  
Vol 11 (15) ◽  
pp. 7148
Author(s):  
Bedada Endale ◽  
Abera Tullu ◽  
Hayoung Shi ◽  
Beom-Soo Kang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Architecture ◽  
Input Data ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Object Classification ◽  
Training Data ◽  
Gradient Descent Algorithm

Unmanned aerial vehicles (UAVs) are being widely utilized for various missions: in both civilian and military sectors. Many of these missions demand UAVs to acquire artificial intelligence about the environments they are navigating in. This perception can be realized by training a computing machine to classify objects in the environment. One of the well known machine training approaches is supervised deep learning, which enables a machine to classify objects. However, supervised deep learning comes with huge sacrifice in terms of time and computational resources. Collecting big input data, pre-training processes, such as labeling training data, and the need for a high performance computer for training are some of the challenges that supervised deep learning poses. To address these setbacks, this study proposes mission specific input data augmentation techniques and the design of light-weight deep neural network architecture that is capable of real-time object classification. Semi-direct visual odometry (SVO) data of augmented images are used to train the network for object classification. Ten classes of 10,000 different images in each class were used as input data where 80% were for training the network and the remaining 20% were used for network validation. For the optimization of the designed deep neural network, a sequential gradient descent algorithm was implemented. This algorithm has the advantage of handling redundancy in the data more efficiently than other algorithms.

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.

scholarly journals Anomaly Detection Using Deep Neural Network for IoT Architecture

2021 ◽  
Vol 11 (15) ◽  
pp. 7050
Author(s):  
Zeeshan Ahmad ◽  
Adnan Shahid Khan ◽  
Kashif Nisar ◽  
Iram Haider ◽  
Rosilah Hassan ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Deep Neural Network ◽  
Detection System ◽  
Traffic Monitoring ◽  
Detection Accuracy ◽  
Learning Models ◽  
Prime Concern ◽  
Entry Points

The revolutionary idea of the internet of things (IoT) architecture has gained enormous popularity over the last decade, resulting in an exponential growth in the IoT networks, connected devices, and the data processed therein. Since IoT devices generate and exchange sensitive data over the traditional internet, security has become a prime concern due to the generation of zero-day cyberattacks. A network-based intrusion detection system (NIDS) can provide the much-needed efficient security solution to the IoT network by protecting the network entry points through constant network traffic monitoring. Recent NIDS have a high false alarm rate (FAR) in detecting the anomalies, including the novel and zero-day anomalies. This paper proposes an efficient anomaly detection mechanism using mutual information (MI), considering a deep neural network (DNN) for an IoT network. A comparative analysis of different deep-learning models such as DNN, Convolutional Neural Network, Recurrent Neural Network, and its different variants, such as Gated Recurrent Unit and Long Short-term Memory is performed considering the IoT-Botnet 2020 dataset. Experimental results show the improvement of 0.57–2.6% in terms of the model’s accuracy, while at the same time reducing the FAR by 0.23–7.98% to show the effectiveness of the DNN-based NIDS model compared to the well-known deep learning models. It was also observed that using only the 16–35 best numerical features selected using MI instead of 80 features of the dataset result in almost negligible degradation in the model’s performance but helped in decreasing the overall model’s complexity. In addition, the overall accuracy of the DL-based models is further improved by almost 0.99–3.45% in terms of the detection accuracy considering only the top five categorical and numerical features.

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