Front Camera Research Articles

Decision Support System for SmartPhone Selection with AHP-VIKOR Method Recommendations

Produce products that have various features and diverse functions, which are able to provide convenience with the reliability of their features and functions. The advantages possessed by SmartPhone become more confident for users to assess the level of product intelligence, the more trustworthy. The purpose of this research is to provide additional knowledge on the selection of SmartPhone to the user in having a product with various benefits. The more criteria that become a barometer, the more difficult it is to choose a product in the form of a SmartPhone. Thus, the right method is needed to perform the selection of the SmartPhone. There are several methods offered to carry out the selection process for SmartPhones, namely the Analytic Hierarchy Process (AHP) method combined with the VIKOR elimination method. Both of these methods are very supportive in the selection process with many types of criteria and their meanings against these criteria. A number of criteria that serve as a barometer for selecting object-based applications are Operating System, Processor, Internal Memory, External Memory, Back Camera, Front Camera, Battery, Cassing Model, Screen Size, Wight and Price. Of the eleven criteria have two different characteristics of understanding. The results of this study can be seen explicitly on the selection of SmartPhones through the acquisition of the smallest Qi index with the three highest ratings, namely the first ranked Samsung Galaxy A3 (0.00) the second is the Xiaomi Mi 4C with an index of 0.19, the third is the Lenovo Vibe K5 Plus with index 0.31. Thus it can be said that the collaboration of the AHP and VIKOR Elimination methods is able to provide optimal decision-making support.

Moiré effect in multilayered 3D lattice.

The moiré effect was observed in a multilayered three-dimensional (3D) structure with voxels at the points of a simple cubic lattice. Visual corridors result from the moiré effect. The frontal camera's corridors appear at distinctive angles with rational tangents. We analyzed the effects of distance, size, and thickness. We carried out computer simulation and physical experiments; both confirmed the distinctive angles of the moiré patterns for the three camera locations near the facet, edge, and vertex. The conditions for the moiré patterns to appear in the cubic lattice were formulated. The results can be used in crystallography and in minimization of the moiré effect in LED-based volumetric 3D displays.

P‐13.8: A solution on improving display effect of UDC panel

As a breakthrough technology, UDC (Under‐Display Camera) can achieve full display, also can increase panel proportion of mobile phones. It is the most likely way to balance the contradiction between front camera and display integrity settings. However, due to the design difference between the main and UDC areas, the display consistency is an important factor affecting the display effect and production yield of UDC panel. To solve this problem, this paper analyzes the reasons for the inconsistency between the main and UDC areas, as well as the defects of the current main solutions. An advanced debug scheme is proposed. It can significantly improve the accuracy of display brightness of UDC area, so as to improve the display effect of UDC panel.

Behavioral and Physiological Signals-Based Deep Multimodal Approach for Mobile Emotion Recognition

With the rapid development of mobile and wearable devices, it is increasingly possible to access users' affective data in a more unobtrusive manner. On this basis, researchers have proposed various systems to recognize user's emotional states. However, most of these studies rely on traditional machine learning techniques and a limited number of signals, leading to systems that either do not generalize well or would frequently lack sufficient information for emotion detection in realistic scenarios. In this paper, we propose a novel attention-based LSTM system that uses a combination of sensors from a smartphone (front camera, microphone, touch panel) and a wristband (photoplethysmography, electrodermal activity, and infrared thermopile sensor) to accurately determine user's emotional states. We evaluated the proposed system by conducting a user study with 45 participants. Using collected behavioral (facial expression, speech, keystroke) and physiological (blood volume, electrodermal activity, skin temperature) affective responses induced by visual stimuli, our system was able to achieve an average accuracy of 89.2% for binary positive and negative emotion classification under leave-one-participant-out cross-validation. Furthermore, we investigated the effectiveness of different combinations of data signals to cover different scenarios of signal availability.

Performance Analysis for Human Gait Recognition from different Viewpoints

Human gait, a new biometric aimed at recognizing people based on how they walk, has become increasingly important in visual surveillance application. However, one camera at a single view point gait data has commonly been explored, this has not always sufficient enough in the environment of deployment. This research proposes gait analysis as a solution for subjects’ identification across a network of cameras from different viewpoints. Gait signature of a person is created from Temporal and spatial metrics extracted from modal, such as length of trunk, shin and deviation in the limb angles or the amplitude of a person’s walking pattern and these are transformed into a self-similarity matrix. The method of spatio-temporal correlation is to detect the human gait in successive video sequences. Performance evaluation of the system was carried out with CMU (Carnegie Mellon University) Motion of Body (MoBo) database. The results reveal that gait performance analysis of the proposed system is possible even without knowing the position of the camera or the stance of the subject. This research shows that the derived gait parameters suggest that gait may be successfully employed for individuals' identification with cameras surveillance in different view point scenario which resulted in an average recognition rate of 73.3% for persons walking from right camera viewpoints, 80.0% from persons walking from the left camera viewpoint, 60.0% from persons walking from the rare camera viewpoint and 66.67% from persons walking from the front camera viewpoint. This implies that the system performs better from the left camera viewpoint.

Autonomous sequential surgical skills assessment for the peg transfer task in a laparoscopic box-trainer system with three cameras

AbstractIn laparoscopic surgery, surgeons should develop several manual laparoscopic skills before carrying out real operative procedures using a low-cost box trainer. The Fundamentals of Laparoscopic Surgery (FLS) program was developed as a program to assess fundamental knowledge and surgical skills, required for basic laparoscopic surgery. The peg transfer task is a hands-on exam in the FLS program that assists a trainee to understand the relative minimum amount of grasping force necessary to move the pegs from one place to another place without dropping them. In this paper, an autonomous, sequential assessment algorithm based on deep learning, a multi-object detection method, and, several sequential If-Then conditional statements have been developed to monitor each step of a surgeon’s performance. Images from three different cameras are used to assess whether the surgeon executes the peg transfer task correctly and to display a notification on any errors on the monitor immediately. This algorithm improves the performance of a laparoscopic box-trainer system using top, side, and front cameras and removes the need for any human monitoring during a peg transfer task. The developed algorithm can detect each object and its status during a peg transfer task and notifies the resident about the correct or failed outcome. In addition, this system can correctly determine the peg transfer execution time, and the move, carry, and dropped states for each object by the top, side, and front-mounted cameras. Based on the experimental results, the proposed surgical skill assessment system can identify each object at a high score of fidelity, and the train-validation total loss for the single-shot detector (SSD) ResNet50 v1 was about 0.05. Also, the mean average precision (mAP) and Intersection over Union (IoU) of this detection system were 0.741, and 0.75, respectively. This project is a collaborative research effort between the Department of Electrical and Computer Engineering and the Department of Surgery, at Western Michigan University.

Apple in My Eyes (AIME): Liveness Detection for Mobile Security Using Corneal Specular Reflections

The demand for contactless biometric authentication has significantly increased during the COVID-19 pandemic and beyond to prevent the spread of Coronavirus. The global pandemic unexpectedly affords a greater opportunity for contactless authentication, but iris and facial recognition biometrics have many usability, security, and privacy challenges, including mask-wearing and presentation attacks (PAs). Mainly, liveness detection against spoofing is notably a challenging task as various biometric authentication methods cannot efficiently assess the real user’s physical presence in unsupervised environments. Although several face anti-spoofing methods have been proposed using add-on sensors, dynamic facial texture features, and 3-D mapping, most of them require expensive sensors and substantial computational resources, or fail to detect sophisticated 3-D face spoofing. This article presents a software-based facial liveness detection method named Apple in My Eyes (AIME). AIME is intended to detect the liveness against spoofing for mobile device security using challenge-response testing. AIME generates various screen patterns as authentication challenges, then passively detects corneal-specular reflection responses from human eyes using a frontal camera and analyzes the detected reflections using lightweight machine learning techniques. AIME system components include challenge and pattern detection, feature extraction and classification, and data augmentation and training. We have implemented AIME as a cross-platform application compatible with Android, iOS, and the Web. Our comprehensive experimental results reveal that AIME detects liveness with high accuracy at around 200-ms against different types of sophisticated PAs. AIME can also efficiently detect liveness in multiple contactless biometric authentications without any costly extra sensors nor involving users’ active responses.

Smart Face Shield: A Sensor-Based Wearable Face Shield Utilizing Computer Vision Algorithms

The study aims the development of a wearable device to combat the onslaught of covid-19. Likewise, to enhance the regular face shield available in the market. Furthermore, to raise awareness of the health and safety protocols initiated by the government and its affiliates in the enforcement of social distancing with the integration of computer vision algorithms. The wearable device was composed of various hardware and software components such as a transparent polycarbonate face shield, microprocessor, sensors, camera, thin-film transistor on-screen display, jumper wires, power bank, and python programming language. The algorithm incorporated in the study was object detection under computer vision machine learning. The front camera with OpenCV technology determines the distance of a person in front of the user. Utilizing TensorFlow, the target object identifies and detects the image or live feed to get its bounding boxes. The focal length lens requires the determination of the distance from the camera to the target object. To get the focal length, multiply the pixel width by the known distance and divide it by the known width (Rosebrock, 2020). The deployment of unit testing ensures that the parameters are valid in terms of design and specifications.

Розробка алгоритма моніторингу стану водія за допомогою Аndroid-застосунку з метою підвищення рівня активної безпеки

The purpose of the research is the analysis of existing systems for monitoring the driver's condition using an Android mobile application to increase the level of active safety with the development of an algorithm for monitoring the driver's condition. At the same time, such parameters as turns and tilts of the head, duration of eyelid blinking, yawning are taken into account. As a result, the state of drowsiness, impaired attention, and drunkenness of the driver is analyzed. The article analyzes the methods of monitoring the dangerous condition of the driver during the movement of the vehicle. The list of programs used in this case is given. The Drive Safely mobile application was chosen for the research, which is based on monitoring the condition of the driver's eyes and mouth. Based on research, eye PERCLOS should not exceed 28% and mouth PERCLOS should not exceed 30%. An algorithm for recognizing emergency situations in the vehicle cabin based on the image of the driver's eyes and mouth from the front camera of a smartphone has been developed. As a result of the analysis of existing Android applications, an algorithm for recognizing emergency situations in the cabin of the vehicle was developed. The algorithm was obtained based on the image of the smartphone's front camera. This algorithm takes into account all possible dangerous conditions of the driver behind the wheel of the vehicle. As a result of the conducted research, an analysis of the existing systems for monitoring the driver's condition using the Drive Safely mobile Android application was carried out. As a result of the analysis of existing Android applications, an algorithm for recognizing emergency situations in the cabin of the vehicle was developed. The use of this algorithm in the program and the connection of its results to the cloud environment will allow it to self-improve and increase the efficiency of its operation, thus increasing the level of active security.

Investigation of the thermal comfort of the sportswear by standing thermal manikin and thermal imaging techniques

Garments should not overload the body during activity and should be chosen in accordance with the ambient conditions. Especially in active sports, sportswear should make a person feel comfortable and increase performance by easily removing excess heat and moisture from the body without interrupting physical activity. In this study, five T-shirts with the same size and weight characteristics, but in different yarn types were produced and analysed. The thermal and water vapour resistance properties of these garments were measured using a thermal manikin system. In addition, thermal camera images were taken at 10-minute intervals during the 50-minute wear trial programme, and skin temperatures were measured from the upper front body and the upper back body with two sensors. The results showed that POS and PM-coded garments made of polyester with low clothing insulation (clo) values were statistically different from CS and CPS coded cotton-containing garments in terms of front and back surface thermal camera images. It was observed that cotton-containing garments were more uncomfortable than polyester and Tencel owing to their higher thermal resistance, garment surface temperature, and skin temperature values. The Tencel garment was measured similarly to the CS and CPS coded garments at the beginning of the activity and measured similarly to the POS and PM coded garments at the end of the activity on both surfaces.

Where Are the Dots: Hardening Face Authentication on Smartphones With Unforgeable Eye Movement Patterns

With the ubiquitous adoption, mobile face authentication systems have been facing constant security challenges, particularly the spoofing risks. Except for those using specialized hardware, existing proposals for face anti-spoofing on mainstream smartphones either leverage people’s 3D face characteristics or various facial expressions. While showing progress towards more resilient face authentication, they are still vulnerable to recent advanced attacks (e.g., 3D mask attacks, video attacks, etc.). This paper presents GazeGuard, an on-device face anti-spoofing system that leverages unpredictable and unforgeable eye movement patterns to provide strong security guarantees against all known attacks. Targeting mainstream smartphones, GazeGuard is designed to conduct eye movement-based authentication using only 2D front cameras. Specifically, by presenting a series of short-lasting random dots on the screen (named gazecode), GazeGuard simultaneously captures a user’s gaze responses and the corresponding deformed periocular features to ensure both the freshness and correctness for the anti-spoofing face authentication. We have extensively tested GazeGuard’s performance over 50 volunteers. Using a 4-digit gazecode (just four random dots), GazeGuard achieves an average 90.39% authentication accuracy and 81.57 out of 100 System Usability Scale (SUS) scores. Under the same settings, GazeGuard achieves detection accuracy of 95.72% for image attack, 95.59% for video attack, 99.73% for 3D mask attack, and 100% for physical adversarial attack.

Iterative Range and Road Parameters Estimation Using Monocular Camera on Highways

Lane Keeping Assistance and Adaptive Cruise Control in Advanced Driving Assistance Systems (ADAS) requires road parameters for better control of the vehicle. Therefore, in this paper we present the Iterative Least Squares with Optimization (ILSO) method based on a monocular front camera which estimates the horizontal, vertical parameters, lateral slope and the camera’s distances to the left/right line as road parameters and longitudinal range. The ILSO algorithm is smartly divided into two steps LS and optimization depending on their estimation capabilities. First of all, the horizontal and vertical parameters, lateral slope, and distances to the left and right lines are estimated by Least Squares (LS). Then, the longitudinal range is estimated by convex optimization. LS and convex optimization are executed in an alternating manner. ILSO is in advance prior to changes on roads and a simple technique due to its analytical solution which does not require complex geometric calculations and does not need a match between left and right lines. In a simulation environment, given the noisy road lines, ILSO outperforms covariance based and multilayer perceptron (MLP) based methods under different noise variances. In addition, ILSO provides plausible numerical results in both virtual and real road images.

Kombinasi Penerapan Metode WASPAS dan Rank Order Centroid (ROC) dalam Keputusan Pemilihan Teknologi Kamera Ponsel Terbaik

Smartphone camera technology is currently growing rapidly so that it has specifications that support it so that the results obtained are almost on par with the quality of digital cameras. Supported cellphone cameras can be used in learning photography, not necessarily with a digital camera. The increasing number of cellphone choices makes users who want to study photography confused in choosing a cellphone that has the best cellphone camera technology. So that a Decision Support System was applied in this study as a system for obtaining the best cellphone camera technology decisions. In making the right decision on selecting the best cellphone camera technology, it must meet the criteria for Number of Camera Features, Number of Main Cameras, Number of Front Cameras, Main Camera Resolution, Front camera Resolution, Optical Zoom and Quality of Video Recording. Therefore a Decision Support System is needed to solve the problem by applying the ROC (Rank Order Centroid) and WASPAS (Weighted Aggregated Sum Product Assessment) methods which can produce weight and preference values from the first ranking alternative. So that the selection of the best mobile camera technology for the best mobile camera technology can be found in table 7, namely the Samsung Galaxy S22 Ultra which is in the A5 alternative with the highest score of 3.8354 as the first rank

Interactive Lane Keeping System for Autonomous Vehicles Using LSTM-RNN Considering Driving Environments

This paper presents an interactive lane keeping model for an advanced driver assistant system and autonomous vehicle. The proposed model considers not only the lane markers but also the interaction with surrounding vehicles in determining steering inputs. The proposed algorithm is designed based on the Recurrent Neural Network (RNN) with long short-term memory cells, which are configured by the collected driving data. A data collection vehicle is equipped with a front camera, LiDAR, and DGPS. The input features of the RNN consist of lane information, surrounding targets, and ego vehicle states. The output feature is the steering wheel angle to keep the lane. The proposed algorithm is evaluated through similarity analysis and a case study with driving data. The proposed algorithm shows accurate results compared to the conventional algorithm, which only considers the lane markers. In addition, the proposed algorithm effectively responds to the surrounding targets by considering the interaction with the ego vehicle.

The Robotic Multiobject Focal Plane System of the Dark Energy Spectroscopic Instrument (DESI)

A system of 5020 robotic fiber positioners was installed in 2019 on the Mayall Telescope, at Kitt Peak National Observatory. The robots automatically retarget their optical fibers every 10–20 minutes, each to a precision of several microns, with a reconfiguration time of fewer than 2 minutes. Over the next 5 yr, they will enable the newly constructed Dark Energy Spectroscopic Instrument (DESI) to measure the spectra of 35 million galaxies and quasars. DESI will produce the largest 3D map of the universe to date and measure the expansion history of the cosmos. In addition to the 5020 robotic positioners and optical fibers, DESI’s Focal Plane System includes six guide cameras, four wave front cameras, 123 fiducial point sources, and a metrology camera mounted at the primary mirror. The system also includes associated structural, thermal, and electrical systems. In all, it contains over 675,000 individual parts. We discuss the design, construction, quality control, and integration of all these components. We include a summary of the key requirements, the review and acceptance process, on-sky validations of requirements, and lessons learned for future multiobject, fiber-fed spectrographs.

Analysis of habitat canopy cover for bird’s family bucerotidae with canopy capture in pocut meurah intan forest park, aceh besar

Pocut Meurah Intan Grand Forest Park (Tahura) is a form of nature conservation area which is overgrown by various types of plants that are widely used by bird species such as forage trees and nesting trees. Bird species from the Bucerotidae family are one of the fauna that take advantage of the presence of various types of plants in the area. When the existence of plants that function as feeding trees or nesting trees is threatened, the impact will be experienced by various bird species due to habitat changes such as a shortage of life-supporting plants. One of the factors that influence the use of trees in their habitat by birds is the density and canopy cover. The purpose of this study was to analyze the percentage of canopy cover for forage trees and bird nesting trees in the Bucerotidae family in Tahura Pocut Meurah Intan. Descriptive method is the method chosen in this study. The research location stations were determined using purposive sampling. To determine the canopy cover of forage trees and nesting trees, Canopycapture ver.1.0.2 software was used with the Hemispherical Photography method. Hemispherical Photography is a method by calculating the area of the canopy cover using a camera from under the tree canopy. This method produces more accurate data because the area captured by the camera is more specific so that it describes the actual tree canopy cover. Tree canopy cover data collection using Hemispherical Photography using the front camera of the XIAOMI Redmi Note 6 Pro mobile phone with a dual camera resolution of 20 and 2 megapixels with HD resolution (1,280 x 720) at a point of taking photos. This technique is used in a 10 mx 10 m plot and analyzed using Microsoft office 2021 software. This research was conducted in January – February 2021 with a total of 2 stations. The results of this study found 14 species of forage trees and 13 species of nesting trees. The Moraceae family is more dominant in the use of birds from the Bucerotidae family as feed trees, while the Lauraceae family and also the Moraceae family are dominant as nesting trees. The value of tree canopy cover in Tahura Pocut Meurah Intan is 84.00% which belongs to the solid category.

HandyGaze: A Gaze Tracking Technique for Room-Scale Environments using a Single Smartphone

We propose HandyGaze, a 6-DoF gaze tracking technique for room-scale environments that can be carried out by simply holding a smartphone naturally without installing any sensors or markers in the environment. Our technique simultaneously employs the smartphone’s front and rear cameras: The front camera estimates the user’s gaze vector relative to the smartphone, while the rear camera (and depth sensor, if available) performs self-localization by reconstructing a pre-obtained 3D map of the environment. To achieve this, we implemented a prototype that works on iOS smartphones by running an ARKit-based algorithm for estimating the user’s 6-DoF head orientation. We additionally implemented a novel calibration method that offsets the user-specific deviation between the head and gaze orientations. We then conducted a user study (N=10) that measured our technique’s positional accuracy to the gaze target under four conditions, based on combinations of use with and without a depth sensor and calibration. The results show that our calibration method was able to reduce the mean absolute error of the gaze point by 27%, with an error of 0.53 m when using the depth sensor. We also report the target size required to avoid erroneous inputs. Finally, we suggest possible applications such as a gaze-based guidance application for museums.

Pipe Spatter Detection and Grinding Robot

This paper proposes a robotic system that automatically identifies and removes spatters generated while removing the back-bead left after the electric resistance welding of the outer and inner surfaces during pipe production. Traditionally, to remove internal spatters on the front and rear of small pipes with diameters of 18–25 cm and lengths of up to 12 m, first, the spatter locations (direction and length) are determined using a camera that is inserted into the pipe, and then a manual grinder is introduced up to the point where spatters were detected. To optimize this process, the proposed robotic system automatically detects spatters by analyzing the images from a front camera and removes them, using a grinder module, based on the spatter location and the circumferential coordinates provided by the detection step. The proposed robot can save work time by reducing the required manual work from two points (the front and back of the pipe) to a single point. Image recognition enables the detection of spatters with sizes between 0.1 and 10 cm with 94% accuracy. The internal average roughness, Ra, of the pipe was confirmed to be 1 µm or less after the spatters were finally removed.

Understanding and Creating Spatial Interactions with Distant Displays Enabled by Unmodified Off-The-Shelf Smartphones

Over decades, many researchers developed complex in-lab systems with the overall goal to track multiple body parts of the user for a richer and more powerful 2D/3D interaction with a distant display. In this work, we introduce a novel smartphone-based tracking approach that eliminates the need for complex tracking systems. Relying on simultaneous usage of the front and rear smartphone cameras, our solution enables rich spatial interactions with distant displays by combining touch input with hand-gesture input, body and head motion, as well as eye-gaze input. In this paper, we firstly present a taxonomy for classifying distant display interactions, providing an overview of enabling technologies, input modalities, and interaction techniques, spanning from 2D to 3D interactions. Further, we provide more details about our implementation—using off-the-shelf smartphones. Finally, we validate our system in a user study by a variety of 2D and 3D multimodal interaction techniques, including input refinement.

Efficacy of a video-assisted teaching program on the effects of selfie-taking behaviour and its impact on health on nursing students at Hillside College of Nursing in Bangalore

Background: Smart phones are no longer only a communication device but serve as a substitute for the home computer and have internet related access as well as a camera that with its high resolution now substitutes the camera, we call it as “selfie camera”. Selfie have now become a major trend and smart phone manufacturers are ensuring that they provide good front cameras in phone and technology is adapting this social trend of self-portraits to rule the world.Methods: For this study, an evaluative technique was applied. The data is collected using a one-group pre-test and post-test design. The video assisted teaching programme regarding impact of selfie taking behavior and its impact on health was the study's independent variable, while the attitude of nursing students regarding selfie taking behavior and its impact on health was the dependent variable. The study was conducted from April 2022 to June 2022 among 60 subjects; the collected data was analyzed and interpreted using descriptive and inferential statistics.Results: The present study reveals that the overall behavioral scores of respondents were found to be 51.33% with standard deviation 4.59 in pre-test. The overall behavioral scores of respondents were found to be 40.1% with standard deviation 3.142 in post-test. The obtained "t" value 17.385 is greater than the table value at 0.01 level of significance. Therefore, "t" value is found to be significant. It means there is reduction in behavioral level of nursing student.Conclusions: Study demonstrated that the video teaching programme on selfies taking behavior and its impact on health is effective in decreasing the behavioral level of nursing students.