Face Mask Detection Using Deep Convolutional Neural Network and MobileNetV2-Based Transfer Learning

publishedVersio

Internet of Things with Deep Learning-Based Face Recognition Approach for Authentication in Control Medical Systems

Internet of Things (IoT) with deep learning (DL) is drastically growing and plays a significant role in many applications, including medical and healthcare systems. It can help users in this field get an advantage in terms of enhanced touchless authentication, especially in spreading infectious diseases like coronavirus disease 2019 (COVID-19). Even though there is a number of available security systems, they suffer from one or more of issues, such as identity fraud, loss of keys and passwords, or spreading diseases through touch authentication tools. To overcome these issues, IoT-based intelligent control medical authentication systems using DL models are proposed to enhance the security factor of medical and healthcare places effectively. This work applies IoT with DL models to recognize human faces for authentication in smart control medical systems. We use Raspberry Pi (RPi) because it has low cost and acts as the main controller in this system. The installation of a smart control system using general-purpose input/output (GPIO) pins of RPi also enhanced the antitheft for smart locks, and the RPi is connected to smart doors. For user authentication, a camera module is used to capture the face image and compare them with database images for getting access. The proposed approach performs face detection using the Haar cascade techniques, while for face recognition, the system comprises the following steps. The first step is the facial feature extraction step, which is done using the pretrained CNN models (ResNet-50 and VGG-16) along with linear binary pattern histogram (LBPH) algorithm. The second step is the classification step which can be done using a support vector machine (SVM) classifier. Only classified face as genuine leads to unlock the door; otherwise, the door is locked, and the system sends a notification email to the home/medical place with detected face images and stores the detected person name and time information on the SQL database. The comparative study of this work shows that the approach achieved 99.56% accuracy compared with some different related methods.publishedVersio

A Simple and Efficient Deep Learning-Based Framework for Automatic Fruit Recognition

Accurate detection and recognition of various kinds of fruits and vegetables by using the artificial intelligence (AI) approach always remain a challenging task due to similarity between various types of fruits and challenging environments such as lighting and background variations. Therefore, developing and exploring an expert system for automatic fruits’ recognition is getting more and more important after many successful approaches; however, this technology is still far from being mature. The deep learning-based models have emerged as state-of-the-art techniques for image segmentation and classification and have a lot of promise in challenging domains such as agriculture, where they can deal with the large variability in data better than classical computer vision methods. In this study, we proposed a deep learning-based framework to detect and recognize fruits and vegetables automatically with difficult real-world scenarios. The proposed method might be helpful for the fruit sellers to identify and differentiate various kinds of fruits and vegetables that have similarities. The proposed method has applied deep convolutional neural network (DCNN) to the undertakings of distinguishing natural fruit images of the Gilgit-Baltistan (GB) region as this area is famous for fruits’ production in Pakistan as well as in the world. The experimental outcomes demonstrate that the suggested deep learning algorithm has the effective capability of automatically recognizing the fruit with high accuracy of 96%. This high accuracy exhibits that the proposed approach can meet world application requirements.publishedVersio

Evaluation of Deep Learning and Conventional Approaches for Image Recaptured Detection in Multimedia Forensics

Image recaptured from a high-resolution LED screen or a good quality printer is difficult to distinguish from its original counterpart. The forensic community paid less attention to this type of forgery than to other image alterations such as splicing, copy-move, removal, or image retouching. It is significant to develop secure and automatic techniques to distinguish real and recaptured images without prior knowledge. Image manipulation traces can be hidden using recaptured images. For this reason, being able to detect recapture images becomes a hot research topic for a forensic analyst. The attacker can recapture the manipulated images to fool image forensic system. As far as we know, there is no prior research that has examined the pros and cons of up-to-date image recaptured techniques. The main objective of this survey was to succinctly review the recent outcomes in the field of image recaptured detection and investigated the limitations in existing approaches and datasets. The outcome of this study provides several promising directions for further significant research on image recaptured detection. Finally, some of the challenges in the existing datasets and numerous promising directions on recaptured image detection are proposed to demonstrate how these difficulties might be carried into promising directions for future research. We also discussed the existing image recaptured datasets, their limitations, and dataset collection challenges.publishedVersio

Requirement Engineering : A comparision between Traditional requirement elicitation techniqes with user story

Requirements are features or attributes which we discover at the initial stage of building a product. Requirements describe the system functionality that satisfies customer needs. An incomplete and inconsistent requirement of the project leads to exceeding cost or devastating the project. So there should be a process for obtaining sufficient, accurate and refining requirements such a process is known as requirement elicitation. Software requirement elicitation process is regarded as one of the most important parts of software development. During this stage it is decided precisely what should be built. There are many requirements elicitation techniques however selecting the appropriate technique according to the nature of the project is important for the successful development of the project. Traditional software development and agile approaches to requirements elicitation are suitable in their own context. With agile approaches a high-level, low formal form of requirement specification is produced and the team is fully prepared to respond unavoidable changes in these requirements. On the other hand in traditional approach project could be done more satisfactory with a plan driven well documented specification. Agile processes introduced their most broadly applicable technique with user stories to express the requirements of the project. A user story is a simple and short written description of desired functionality from the perspective of user or owner. User stories play an effective role on all time constrained projects and a good way to introducing a bit of agility to the projects. Personas can be used to fill the gap of user stories

Internet of Things with Deep Learning-Based Face Recognition Approach for Authentication in Control Medical Systems

Internet of Things (IoT) with deep learning (DL) is drastically growing and plays a significant role in many applications, including medical and healthcare systems. It can help users in this field get an advantage in terms of enhanced touchless authentication, especially in spreading infectious diseases like coronavirus disease 2019 (COVID-19). Even though there is a number of available security systems, they suffer from one or more of issues, such as identity fraud, loss of keys and passwords, or spreading diseases through touch authentication tools. To overcome these issues, IoT-based intelligent control medical authentication systems using DL models are proposed to enhance the security factor of medical and healthcare places effectively. This work applies IoT with DL models to recognize human faces for authentication in smart control medical systems. We use Raspberry Pi (RPi) because it has low cost and acts as the main controller in this system. The installation of a smart control system using general-purpose input/output (GPIO) pins of RPi also enhanced the antitheft for smart locks, and the RPi is connected to smart doors. For user authentication, a camera module is used to capture the face image and compare them with database images for getting access. The proposed approach performs face detection using the Haar cascade techniques, while for face recognition, the system comprises the following steps. The first step is the facial feature extraction step, which is done using the pretrained CNN models (ResNet-50 and VGG-16) along with linear binary pattern histogram (LBPH) algorithm. The second step is the classification step which can be done using a support vector machine (SVM) classifier. Only classified face as genuine leads to unlock the door; otherwise, the door is locked, and the system sends a notification email to the home/medical place with detected face images and stores the detected person name and time information on the SQL database. The comparative study of this work shows that the approach achieved 99.56% accuracy compared with some different related methods

A Simple and Efficient Deep Learning-Based Framework for Automatic Fruit Recognition

Accurate detection and recognition of various kinds of fruits and vegetables by using the artificial intelligence (AI) approach always remain a challenging task due to similarity between various types of fruits and challenging environments such as lighting and background variations. Therefore, developing and exploring an expert system for automatic fruits’ recognition is getting more and more important after many successful approaches; however, this technology is still far from being mature. The deep learning-based models have emerged as state-of-the-art techniques for image segmentation and classification and have a lot of promise in challenging domains such as agriculture, where they can deal with the large variability in data better than classical computer vision methods. In this study, we proposed a deep learning-based framework to detect and recognize fruits and vegetables automatically with difficult real-world scenarios. The proposed method might be helpful for the fruit sellers to identify and differentiate various kinds of fruits and vegetables that have similarities. The proposed method has applied deep convolutional neural network (DCNN) to the undertakings of distinguishing natural fruit images of the Gilgit-Baltistan (GB) region as this area is famous for fruits’ production in Pakistan as well as in the world. The experimental outcomes demonstrate that the suggested deep learning algorithm has the effective capability of automatically recognizing the fruit with high accuracy of 96%. This high accuracy exhibits that the proposed approach can meet world application requirements

Evaluation of Deep Learning and Conventional Approaches for Image Recaptured Detection in Multimedia Forensics

Image recaptured from a high-resolution LED screen or a good quality printer is difficult to distinguish from its original counterpart. The forensic community paid less attention to this type of forgery than to other image alterations such as splicing, copy-move, removal, or image retouching. It is significant to develop secure and automatic techniques to distinguish real and recaptured images without prior knowledge. Image manipulation traces can be hidden using recaptured images. For this reason, being able to detect recapture images becomes a hot research topic for a forensic analyst. The attacker can recapture the manipulated images to fool image forensic system. As far as we know, there is no prior research that has examined the pros and cons of up-to-date image recaptured techniques. The main objective of this survey was to succinctly review the recent outcomes in the field of image recaptured detection and investigated the limitations in existing approaches and datasets. The outcome of this study provides several promising directions for further significant research on image recaptured detection. Finally, some of the challenges in the existing datasets and numerous promising directions on recaptured image detection are proposed to demonstrate how these difficulties might be carried into promising directions for future research. We also discussed the existing image recaptured datasets, their limitations, and dataset collection challenges

Spectral quality assessment of Landsat 8 and Sentinel 2 bands for glacier identification in Upper Indus Basin

Glacier studies of Hindu Kush Karakoram Himalaya (HKKH) are inadequate where, the stability of glaciers in the Upper Indus Basin (UIB) of HKKH is known for anomaly studies. Despite of satellite based synoptic measuring schema, the quality of glacier anomaly estimate is always on debate. The advancement in Operational Land Imager (OLI) and Multi Spectral Instrument (MSI) offers the potential future of glacier measurement in UIB. Therefore, this study assesses the quality of OLI and MSI in mapping the glacier anomaly for glaciers of Hunzza in UIB. The methodology is based on acquisition of Landsat Enhanced Thematic Mapper Plus (ETM+) Level 1C and OLI Level 2 data, while for Sentinel MSI Level 2A data was derived using Level 1C. Both OLI and MSI were calibrated with uncertainty of 3% than 5% of the raw ETM+. Glacier outlines extracted from the Randolph Glacier Inventory and the snow line altitude (SLA) demarcated through contour generation from Global Digital Elevation Model (GDEM) to differentiate permanent snow and clear ice in the overall glacier polygon. Reflectance of each band was derived and Normalized Snow Differential Index (NDSI) calculated. Statistics applied in spectral quality assessment for glacier parameters. Overall glacier surface exhibited range of reflectance about 0.08 to 0.12, 0.07 to 0.11 and 0.06 to 0.09 at visible bands of OLI that was differed about 20%, 22% and 25% than that of MSI. Where, in infrared band both sensors agreed by the reflectance of 0.10. Reflectance correlation between both sensors derived as 0.7 to 0.9 at visible band and 0.5 to 0.6 at infrared which, allows clear discrimination between the clear ice and snow. But the overlap of reflectance within 0.2 to 0.5 and 0.35 and 1.0 in MSI bands led to erroneous identification. To complement the results, NDSI of OLI with 0 to 0.25 and 0.75 to 1.0 becomes good indicator to distinguish different glacier features with disadvantage of inconsistent in MSI. These results clearly show that OLI and MSI have promising capability to map glacier anomaly and both variants can be synergized for better interpretation in climacterically intrinsic high-altitude zone of UIB