Landmark Algorithm Research Articles

Quarantine Order Violators System using Face Recognition (FACID)

Face recognition technology is commonly utilized for user authentication and verification by analysing a digital image of a person's face and matching it against a database of faces for identification purposes. The COVID-19 pandemic has led to mandatory quarantines and the use of quarantine bracelets for some individuals, which can be time-consuming and require a lot of effort. Face recognition technology can help raise awareness about the current situation and alleviate some of the burden associated with quarantine measures. The aim of this research is to concentrate on the face recognition of individuals with a history of quarantine, as a measure to prevent the spread of COVID-19 in educational institutions like universities, colleges, and schools. This research study concentrates on individuals with a history of quarantine orders. The system will employ the Histograms of Oriented Gradients (HOG) algorithm for face detection. Additionally, the system will utilize the Face Landmark Algorithm to compare the 128-d vector with images stored locally. The system will make use of the Helen face collection dataset for its data requirements. The aim of this study is to explore and identify techniques for detecting individuals who violate quarantine measures and issuing notifications through the proposed system. By implementing this system, it could contribute to creating a more secure environment within the educational institution and potentially reduce the spread of the virus.

Clinical validity and precision of deep learning-based cone-beam computed tomography automatic landmarking algorithm.

This study was performed to assess the clinical validity and accuracy of a deep learning-based automatic landmarking algorithm for cone-beam computed tomography (CBCT). Three-dimensional (3D) CBCT head measurements obtained through manual and automatic landmarking were compared. A total of 80 CBCT scans were divided into 3 groups: non-surgical (39 cases); surgical without hardware, namely surgical plates and mini-screws (9 cases); and surgical with hardware (32 cases). Each CBCT scan was analyzed to obtain 53 measurements, comprising 27 lengths, 21 angles, and 5 ratios, which were determined based on 65 landmarks identified using either a manual or a 3D automatic landmark detection method. In comparing measurement values derived from manual and artificial intelligence landmarking, 6 items displayed significant differences: R U6CP-L U6CP, R L3CP-L L3CP, S-N, Or_R-R U3CP, L1L to Me-GoL, and GoR-Gn/S-N (P<0.05). Of the 3 groups, the surgical scans without hardware exhibited the lowest error, reflecting the smallest difference in measurements between human- and artificial intelligence-based landmarking. The time required to identify 65 landmarks was approximately 40-60 minutes per CBCT volume when done manually, compared to 10.9 seconds for the artificial intelligence method (PC specifications: GeForce 2080Ti, 64GB RAM, and an Intel i7 CPU at 3.6 GHz). Measurements obtained with a deep learning-based CBCT automatic landmarking algorithm were similar in accuracy to values derived from manually determined points. By decreasing the time required to calculate these measurements, the efficiency of diagnosis and treatment may be improved.

ViLiDEx- A Lip Extraction Algorithm for Lip Reading

Technology is evolving at an immense speed every day. In the lap of technology, computer vision and machine learning are also growing fast. Many real time applications are running without human interaction just because of Computer vision and machine learning. In this paper, we are using computer vision and machine learning for lip feature extraction for Gujarati language. For this task we have created dataset GVLetters for Gujarati alphabets. We have taken videos of 24 speakers for 33 alphabets of Guajarati language. Face landmark algorithm from dlib is used for deriving ViLiDEx (Vibhavari’s algorithm for Lip Detection and Extraction). ViLiDEx is applied for 24 speakers and 5 alphabets from each class (Guttural, Palatal, Retroflex, Dental and Labial). This algorithm calculates total number of frames for each speaker, keep 20/25 frames as a dataset and removes extra frames. Depending on number of frames, frame numbers divisible by prime numbers are chosen for removal.

Accuracy and reliability of automated three-dimensional facial landmarking in medical and biological studies. A systematic review.

3D facial landmarking is becoming a fundamental part of clinical and biological applications. Manual landmarking is time consuming and prone to cumulative errors, so attempts have been made to automate 3D facial landmarking. However, data in the literature are sparse. The objectives of this study are to investigate current evidence for the accuracy and reliability of various 3D facial automated landmarking methods used in medical and biological studies and evaluate their performance against the manual annotation method. Electronic and manual searches of the literature were performed in April 2021. Only studies that were published in English and evaluated the accuracy of automated landmarking algorithms in 3D facial images for medical or biological settings were included. Two authors independently screened the articles for eligibility. The QUADAS-2 tool was used for the quality analysis of the included studies. Due to the heterogeneity of the selected studies, a meta-analysis was not possible, so a narrative synthesis of the findings was performed. From 1002 identified records, after applying the inclusion and exclusion criteria, 14 articles were ultimately selected, read, and critically analysed. Different algorithms were used for the automated 3D landmarking of various numbers of facial landmarks ranging from 10 to 29 landmarks. The average difference between the manual and automated methods ranged from 0.67 to 4.73 mm, and the best performance was achieved using deep learning models. Poor study design and inadequate reporting were found in the implementation of the reference standards and population selection for the intended studies, which could have led to overfitting of the tested algorithm. This systematic review was limited by the quality of the included studies and uncovered several methodological limitations evident in the corresponding literature. Compared to manual landmarking, automated Landmark localization of individual facial landmarks reported in the literature is not accurate enough to allow their use for clinical purposes. This result indicates that automatic facial landmarking is still developing, and further studies are required to develop a system that could match or exceed the performance of the current gold standard. PROSPERO: CRD42021241531.

Dangerous Driving Detection Based on IoT

Today, finding drowsy drivers is perhaps the most important step in stopping any traffic accident, everywhere in the globe. The objective of this study was to develop a smart alert approach for intelligent vehicles that can prevent drowsy driving impairment automatically. But being sleepy is a natural bodily occurrence that might occur for a variety of reasons. Therefore, it is necessary to create a reliable alert system to prevent the accident's cause. In this proposed research, we discuss a method for developing a drowsy driver alert system that analyses video stream processing (VSP) utilizing the eye blink concept employing the eye aspect ratio (EAR) and Euclidean distance of the eye. The face landmark algorithm is also employed as a reliable method for detecting eyes. The IoT module sends a warning message together with collision impact and location information when the driver's weariness is recognized, alerting with the help of a voice speaking over the Raspberry Pi monitoring system.

IoT‐Based Smart Alert System for Drowsy Driver Detection

In current years, drowsy driver detection is the most necessary procedure to prevent any road accidents, probably worldwide. The aim of this study was to construct a smart alert technique for building intelligent vehicles that can automatically avoid drowsy driver impairment. But drowsiness is a natural phenomenon in the human body that happens due to different factors. Hence, it is required to design a robust alert system to avoid the cause of the mishap. In this proposed paper, we address a drowsy driver alert system that has been developed using such a technique in which the Video Stream Processing (VSP) is analyzed by eye blink concept through an Eye Aspect Ratio (EAR) and Euclidean distance of the eye. Face landmark algorithm is also used as a proper way to eye detection. When the driver’s fatigue is detected, the IoT module issues a warning message along with impact of collision and location information, thereby alerting with the help of a voice speaking through the Raspberry Pi monitoring system.

Fully Automatic Landmarking of Syndromic 3D Facial Surface Scans Using 2D Images.

3D facial landmarks are known to be diagnostically relevant biometrics for many genetic syndromes. The objective of this study was to extend a state-of-the-art image-based 2D facial landmarking algorithm for the challenging task of 3D landmark identification on subjects with genetic syndromes, who often have moderate to severe facial dysmorphia. The automatic 3D facial landmarking algorithm presented here uses 2D image-based facial detection and landmarking models to identify 12 landmarks on 3D facial surface scans. The landmarking algorithm was evaluated using a test set of 444 facial scans with ground truth landmarks identified by two different human observers. Three hundred and sixty nine of the subjects in the test set had a genetic syndrome that is associated with facial dysmorphology. For comparison purposes, the manual landmarks were also used to initialize a non-linear surface-based registration of a non-syndromic atlas to each subject scan. Compared to the average intra- and inter-observer landmark distances of 1.1 mm and 1.5 mm respectively, the average distance between the manual landmark positions and those produced by the automatic image-based landmarking algorithm was 2.5 mm. The average error of the registration-based approach was 3.1 mm. Comparing the distributions of Procrustes distances from the mean for each landmarking approach showed that the surface registration algorithm produces a systemic bias towards the atlas shape. In summary, the image-based automatic landmarking approach performed well on this challenging test set, outperforming a semi-automatic surface registration approach, and producing landmark errors that are comparable to state-of-the-art 3D geometry-based facial landmarking algorithms evaluated on non-syndromic subjects.

DEVELOPMENT A SYSTEM FOR CLASSIFYING AND RECOGNIZING PERSON’S FACE

The purpose of this article is to summarize the knowledge gained in the development and implementation of a neural network for facial recognition. Neural networks are used to solve complex tasks that require analytical calculations similar to what the human brain does. Machine learning algorithms are the foundation of a neural network. As input, the algorithm receives an image with people's faces, then searches for faces in this image using HOG (Histogram of oriented gradients). The result is images with explicit face structures. To determine unique facial features, the Face landmark algorithm is used, which finds 68 special points on the face. These points can be used to centerthe eyes and mouth for more accurate encoding. To get an accurate “face map” consisting of 128 dimensions, you need to use image encoding. Using the obtained data, the convolutional neural network can determine people's faces using the SVM linear classifier algorithm.

Real-Time Driver Awareness Detection System

Nowadays, drowsiness is the major reason for many road accidents. Due to this fact, different attempts have been made to successfully detect fatigue. In this paper, a computer vision method has been presented to determine the presence of fatigue in a driver’s face. A way for fatigue recognition through the exploitation of facial features has been proposed. A landmark algorithm has been used to finds the marks of the eye edge and then calculate Eye Aspect Ratio (EAR), which is the main threshold parameter to judge whether the driver is sleeping or not. A new approach has been proposed to calculate EAR, which gives more accurate results than the common method. The results illustrate that the proposed method gives maximum detection fluctuation of (0.18), while the common method gives (0.33) detection fluctuation, the proposed system gives robustness against noise, so it can detect face and gives a decision for driver awareness with a noise level of (130) dB. An OpenCV library for image processing and Dlib library for feature extraction with the python IDLE has been utilized.

Blink Detection Based on Pixel Fluctuation Ratio of Eye Image

In this paper, an algorithm of real-time eye blink monitoring using ordinary camera is proposed. This algorithm is based on pixel fluctuation ratio (FRP) of eye region image to detect eye blink. Position the eye area according to the facial landmark algorithm and then convert the image to a grayscale image. According to the point detected by the eye region in the grayscale image, the eye region is divided into three segments from the corner of the eye to the end of the eye. Calculate the value of the pixels in each segment, and find the partial image pixel value fluctuation ratio (FRP), and train the selected training samples with the support vector machine (SVM). We tested it on two data sets, and the blink detection algorithm achieved good results on both data sets.

Face Dynamic Modeling Based on Deep Learning and Feature Extraction

Facial modeling is a key step to model visual effects in special movie effects and computer games. In this paper, a method based on the combination of deep learning and feature extraction is proposed for the modeling of 3D face model. Firstly, the face region is located for the captured face image. And then, the facial feature points are extracted by the landmark algorithm and the Convolutional Neural Network (CNN) is used to classify the facial expressions. Next, a special expression 3D face model is created by the deformation of the standard 3D face model based on the facial expressions classification result. Finally, the 3D face model and the extracted facial feature points are combined to perform personalized adjustment of the 3D model to complete a 3D facial expression animation system. The experimental results show that the proposed method can effectively perform the dynamic 3D face modeling which has high reality.

Gaussian Process Landmarking for Three-Dimensional Geometric Morphometrics

We demonstrate applications of the Gaussian process-based landmarking algorithm proposed in [T. Gao, S.Z. Kovalsky, and I. Daubechies, SIAM Journal on Mathematics of Data Science (2019)] to geometric morphometrics, a branch of evolutionary biology centered at the analysis and comparisons of anatomical shapes, and compares the automatically sampled landmarks with the "ground truth" landmarks manually placed by evolutionary anthropologists; the results suggest that Gaussian process landmarks perform equally well or better, in terms of both spatial coverage and downstream statistical analysis. We provide a detailed exposition of numerical procedures and feature filtering algorithms for computing high-quality and semantically meaningful diffeomorphisms between disk-type anatomical surfaces.

Three-dimensional soft tissue effects of mandibular midline distraction and surgically assisted rapid maxillary expansion: an automatic stereophotogrammetry landmarking analysis

Studies on mandibular midline distraction (MMD) are mostly performed using conventional research methods. Concerning surgically assisted rapid maxillary expansion (SARME), more research is conducted using three-dimensional (3D) techniques. Research on bimaxillary expansion, the combination of MMD and SARME, is reported sparsely. The main objective of this study was to provide a 3D evaluation of soft tissue effects following SARME and/or MMD. Patients who underwent SARME and/or MMD between 2008 and 2013 were included. Stereophotogrammetry was undertaken at the following time points: preoperative (T1), immediately post-distraction (T2), 1year postoperative (T3). An automatic 3D facial landmarking algorithm using two-dimensional Gabor wavelets was applied for the analysis. Twenty patients who had undergone SARME were included, 12 of whom had undergone bimaxillary expansion. Age at the time of surgery ranged from 16 to 47 years. There was a significant downward displacement of soft tissue pogonion. Furthermore, there was a significant mean increase of 2.20mm in inter-alar width and a non-significant mean increase of 1.77mm in inter-alar curvature point width. In conclusion, automatic stereophotogrammetry landmarking analysis of soft tissue effects showed downward displacement of soft tissue pogonion following bimaxillary expansion and transverse widening of the inter-alar width and a tendency towards an increase in inter-alar curvature point width after SARME.

Towards Real-Time Facial Landmark Detection in Depth Data Using Auxiliary Information

Modern facial motion capture systems employ a two-pronged approach for capturing and rendering facial motion. Visual data (2D) is used for tracking the facial features and predicting facial expression, whereas Depth (3D) data is used to build a series of expressions on 3D face models. An issue with modern research approaches is the use of a single data stream that provides little indication of the 3D facial structure. We compare and analyse the performance of Convolutional Neural Networks (CNN) using visual, Depth and merged data to identify facial features in real-time using a Depth sensor. First, we review the facial landmarking algorithms and its datasets for Depth data. We address the limitation of the current datasets by introducing the Kinect One Expression Dataset (KOED). Then, we propose the use of CNNs for the single data stream and merged data streams for facial landmark detection. We contribute to existing work by performing a full evaluation on which streams are the most effective for the field of facial landmarking. Furthermore, we improve upon the existing work by extending neural networks to predict into 3D landmarks in real-time with additional observations on the impact of using 2D landmarks as auxiliary information. We evaluate the performance by using Mean Square Error (MSE) and Mean Average Error (MAE). We observe that the single data stream predicts accurate facial landmarks on Depth data when auxiliary information is used to train the network. The codes and dataset used in this paper will be made available.

Expression robust 3D face landmarking using thresholded surface normals

3D face recognition is an increasing popular modality for biometric authentication, for example in the iPhoneX. Landmarking plays a significant role in region based face recognition algorithms. The accuracy and consistency of the landmarking will directly determine the effectiveness of feature extraction and hence the overall recognition performance. While surface normals have been shown to provide high performing features for face recognition, their use in landmarking has not been widely explored. To this end, a new 3D facial landmarking algorithm based on thresholded surface normal maps is proposed, which is applicable to widely used 3D face databases. The benefits of employing surface normals are demonstrated for both facial roll and yaw rotation calibration and nasal landmarks localization. Results on the Bosphorus, FRGC and BU-3DFE databases show that the detected landmarks possess high within-class consistency and accuracy under different expressions. For several key landmarks the performance achieved surpasses that of state-of-the-art techniques and is also training free and computationally efficient. The use of surface normals therefore provides a useful representation of the 3D surface and the proposed landmarking algorithm provides an effective approach to localising the key nasal landmarks.

Ensemble landmarking of 3D facial surface scans

Landmarking of 3D facial surface scans is an important analysis step in medical and biological applications, such as genome-wide association studies (GWAS). Manual landmarking is often employed with considerable cost and rater dependent variability. Landmarking automatically with minimal training is therefore desirable. We apply statistical ensemble methods to improve automated landmarking of 3D facial surface scans. Base landmarking algorithms using features derived from 3D surface scans are combined using either bagging or stacking. A focus is on low training complexity of maximal 40 training samples with template based landmarking algorithms that have proved successful in such applications. Additionally, we use correlations between landmark coordinates by introducing a search strategy guided by principal components (PCs) of training landmarks. We found that bagging has no useful impact, while stacking strongly improves accuracy to an average error of 1.7 mm across all 21 landmarks in this study, a 22% improvement as compared to a previous, comparable algorithm. Heritability estimates in twin pairs also show improvements when using facial distances from landmarks. Ensemble methods allow improvement of automatic, accurate landmarking of 3D facial images with minimal training which is advantageous in large cohort studies for GWAS and when landmarking needs change or data quality varies.

Computerized Analysis of Mammographic Images for Detection and Characterization of Breast Cancer

Abstract The identification and interpretation of the signs of breast cancer in mammographic images from screening programs can be very difficult due to the subtle and diversified appearance of breast disease. This book presents new image processing and pattern recognition techniques for computer-aided detection and diagnosis of breast cancer in its various forms. The main goals are: (1) the identification of bilateral asymmetry as an early sign of breast disease which is not detectable by other existing approaches; and (2) the detection and classification of masses and regions of architectural distortion, as benign lesions or malignant tumors, in a unified framework that does not require accurate extraction of the contours of the lesions. The innovative aspects of the work include the design and validation of landmarking algorithms, automatic Tabar masking procedures, and various feature descriptors for quantification of similarity and for contour-independent classification of mammographic lesions. Chara...

Compression for Primary Prevention, Treatment, and Prevention of Recurrence of Venous Leg Ulcers: An Evidence-and Consensus-Based Algorithm for Care Across the Continuum.

Chronic venous insufficiency is a prevalent disease that frequently leads to development of venous leg ulcers. While a number of evidence-based clinical practice guidelines have been developed that provide guidance for clinicians when caring for patients with chronic venous insufficiency, they lack adequate detail concerning selection and application of compression for prevention and management of venous leg ulcers. In order to address this need, the WOCN Society appointed a task force to develop an algorithm for compression for primary prevention, treatment, and prevention of recurrent venous leg ulcers in persons with chronic venous insufficiency. The task force used findings from a scoping literature review to identify current best evidence needed to support decision points and pathways within the algorithm. In addition, the task force convened a panel of 20 clinicians and researchers with expertise in lower extremity venous disorders in order to establish consensus around pathways and decision points within the algorithm lacking robust evidence. Following initial construction of the algorithm, a second interdisciplinary group of expert clinicians established content validity and provided additional qualitative feedback used to complete final revisions of the algorithm. This article reviews the process used to create this landmark algorithm, including generation of the evidence- and consensus-based statements used in its construction, the various pathways, and rich supplemental materials embedded within the algorithm, and the process used to establish content validity.

Nasal Patches and Curves for Expression-Robust 3D Face Recognition.

The potential of the nasal region for expression robust 3D face recognition is thoroughly investigated by a novel five-step algorithm. First, the nose tip location is coarsely detected and the face is segmented, aligned and the nasal region cropped. Then, a very accurate and consistent nasal landmarking algorithm detects seven keypoints on the nasal region. In the third step, a feature extraction algorithm based on the surface normals of Gabor-wavelet filtered depth maps is utilised and, then, a set of spherical patches and curves are localised over the nasal region to provide the feature descriptors. The last step applies a genetic algorithm-based feature selector to detect the most stable patches and curves over different facial expressions. The algorithm provides the highest reported nasal region-based recognition ranks on the FRGC, Bosphorus and BU-3DFE datasets. The results are comparable with, and in many cases better than, many state-of-the-art 3D face recognition algorithms, which use the whole facial domain. The proposed method does not rely on sophisticated alignment or denoising steps, is very robust when only one sample per subject is used in the gallery, and does not require a training step for the landmarking algorithm.

EXPLOITING 3D ULTRASOUND FOR FETAL DIAGNOSTIC PURPOSE THROUGH FACIAL LANDMARKING

In the last decade, three-dimensional landmarking has gained attention for different applications, such as face recognition for both identification of suspects and authentication, facial expression recognition, corrective and aesthetic surgery, syndrome study and diagnosis. This work focuses on the last one by proposing a geometrically-based landmark extraction algorithm aimed at diagnosing syndromes on babies before their birth. Pivotal role in this activity is the support provided by physicians and 3D ultrasound tools for working on real faces. In particular, the landmarking algorithm here proposed only relies on descriptors coming from Differential Geometry (Gaussian, mean, and principal curvatures, derivatives, coefficients of first and second fundamental forms, Shape and Curvedness indexes) and is tested on nine facial point clouds referred to nine babies taken by a three-dimensional ultrasound tool at different weeks' gestation. The results obtained, validated with the support of four practitioners, show that the localization is quite accurate. All errors lie in the range between 0 and 3.5 mm and the mean distance for each shell is in the range between 0.6 and 1.6 mm. The landmarks showing the highest errors are the ones belonging to the mouth region. Instead, the most precise landmark is the pronasal, on the nose tip, with a mean distance of 0.55 mm. Relying on current literature, this study is something missing in the state-of-the-art of the field, as present facial studies on 3D ultrasound do not work on automatic landmarking yet.