scholarly journals The Blessing and the Curse of the Noise behind Facial Landmark Annotations

2020 ◽  
Vol 2020 (8) ◽  
pp. 186-1-186-11
Author(s):  
Xiaoyu Xiang ◽  
Yang Cheng ◽  
Shaoyuan Xu ◽  
Qian Lin ◽  
Jan Allebach
Keyword(s):  
Facial Expressions ◽  
Facial Landmark Detection ◽  
The Public ◽  
Facial Landmarks ◽  
Facial Landmark ◽  
Different Types ◽  
Public Dataset ◽  
The Stability ◽  
Public Datasets ◽  
Accuracy And Stability

The evolving algorithms for 2D facial landmark detection empower people to recognize faces, analyze facial expressions, etc. However, existing methods still encounter problems of unstable facial landmarks when applied to videos. Because previous research shows that the instability of facial landmarks is caused by the inconsistency of labeling quality among the public datasets, we want to have a better understanding of the influence of annotation noise in them. In this paper, we make the following contributions: 1) we propose two metrics that quantitatively measure the stability of detected facial landmarks, 2) we model the annotation noise in an existing public dataset, 3) we investigate the influence of different types of noise in training face alignment neural networks, and propose corresponding solutions. Our results demonstrate improvements in both accuracy and stability of detected facial landmarks.

scholarly journals Automatic landmark point detection and tracking for human facial expressions

2021 ◽  
Author(s):  
Yun Tie ◽  
Ling Guan
Keyword(s):  
Facial Expressions ◽  
Similarity Criterion ◽  
Video Sequences ◽  
Minimal Amount ◽  
Expression Recognition ◽  
Scale Invariant ◽  
Facial Landmark Detection ◽  
Facial Region ◽  
Facial Landmarks ◽  
Detection And Tracking

Facial landmarks are a set of salient points, usually located on the corners, tips or mid points of the facial components. Reliable facial landmarks and their associated detection and tracking algorithms can be widely used for representing the important visual features for face registration and expression recognition. In this paper we propose an efficient and robust method for facial landmark detection and tracking from video sequences. We select 26 landmark points on the facial region to facilitate the analysis of human facial expressions. They are detected in the first input frame by the scale invariant feature based detectors. Multiple Differential Evolution-Markov Chain (DE-MC) particle filters are applied for tracking these points through the video sequences. A kernel correlation analysis approach is proposed to find the detection likelihood by maximizing a similarity criterion between the target points and the candidate points. The detection likelihood is then integrated into the tracker’s observation likelihood. Sampling efficiency is improved and minimal amount of computation is achieved by using the intermediate results obtained in particle allocations. Three public databases are used for experiments and the results demonstrate the effectiveness of our method.

scholarly journals Detecting Facial Region and Landmarks at Once via Deep Network

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5360
Author(s):  
Taehyung Kim ◽  
Jiwon Mok ◽  
Euichul Lee
Keyword(s):  
Face Detection ◽  
Detection Method ◽  
Single Shot ◽  
Landmark Detection ◽  
Facial Landmark Detection ◽  
Facial Region ◽  
Facial Landmarks ◽  
Detection Model ◽  
Facial Landmark ◽  
Shot Detection

For accurate and fast detection of facial landmarks, we propose a new facial landmark detection method. Previous facial landmark detection models generally perform a face detection step before landmark detection. This greatly affects landmark detection performance depending on which face detection model is used. Therefore, we propose a model that can simultaneously detect a face region and a landmark without performing the face detection step before landmark detection. The proposed single-shot detection model is based on the framework of YOLOv3, a one-stage object detection method, and the loss function and structure are altered to learn faces and landmarks at the same time. In addition, EfficientNet-B0 was utilized as the backbone network to increase processing speed and accuracy. The learned database used 300W-LP with 64 facial landmarks. The average normalized error of the proposed model was 2.32 pixels. The processing time per frame was about 15 milliseconds, and the average precision of face detection was about 99%. As a result of the evaluation, it was confirmed that the single-shot detection model has better performance and speed than the previous methods. In addition, as a result of using the COFW database, which has 29 landmarks instead of 64 to verify the proposed method, the average normalization error was 2.56 pixels, which was also confirmed to show promising performance.

scholarly journals Automatic landmark point detection and tracking for human facial expressions

2021 ◽  
Author(s):  
Yun Tie ◽  
Ling Guan
Keyword(s):  
Facial Expressions ◽  
Similarity Criterion ◽  
Video Sequences ◽  
Minimal Amount ◽  
Expression Recognition ◽  
Scale Invariant ◽  
Facial Landmark Detection ◽  
Facial Region ◽  
Facial Landmarks ◽  
Detection And Tracking

Facial landmarks are a set of salient points, usually located on the corners, tips or mid points of the facial components. Reliable facial landmarks and their associated detection and tracking algorithms can be widely used for representing the important visual features for face registration and expression recognition. In this paper we propose an efficient and robust method for facial landmark detection and tracking from video sequences. We select 26 landmark points on the facial region to facilitate the analysis of human facial expressions. They are detected in the first input frame by the scale invariant feature based detectors. Multiple Differential Evolution-Markov Chain (DE-MC) particle filters are applied for tracking these points through the video sequences. A kernel correlation analysis approach is proposed to find the detection likelihood by maximizing a similarity criterion between the target points and the candidate points. The detection likelihood is then integrated into the tracker’s observation likelihood. Sampling efficiency is improved and minimal amount of computation is achieved by using the intermediate results obtained in particle allocations. Three public databases are used for experiments and the results demonstrate the effectiveness of our method.

scholarly journals Deep aligned feature extraction for collaborative-representation-based face classification with group dictionary selection

2020 ◽  
Vol 17 (6) ◽  
pp. 172988142096757
Author(s):  
Li Mao ◽  
Delei Zhang ◽  
Youming Chen ◽  
Tao Zhang ◽  
Xiaoning Song
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Sparse Representation ◽  
Image Feature ◽  
Collaborative Representation ◽  
Landmark Detection ◽  
Facial Landmark Detection ◽  
Facial Landmarks ◽  
Face Classification ◽  
Facial Landmark

Face recognition plays an important role in many robotic and human–computer interaction systems. To this end, in recent years, sparse-representation-based classification and its variants have drawn extensive attention in compress sensing and pattern recognition. For image classification, one key to the success of a sparse-representation-based approach is to extract consistent image feature representations for the images of the same subject captured under a wide spectrum of appearance variations, for example, in pose, expression and illumination. These variations can be categorized into two main types: geometric and textural variations. To eliminate the difficulties posed by different appearance variations, the article presents a new collaborative-representation-based face classification approach using deep aligned neural network features. To be more specific, we first apply a facial landmark detection network to an input face image to obtain its fine-grained geometric information in the form of a set of 2D facial landmarks. These facial landmarks are then used to perform 2D geometric alignment across different face images. Second, we apply a deep neural network for facial image feature extraction due to the robustness of deep image features to a variety of appearance variations. We use the term deep aligned features for this two-step feature extraction approach. Last, a new collaborative-representation-based classification method is used to perform face classification. Specifically, we propose a group dictionary selection method for representation-based face classification to further boost the performance and reduce the uncertainty in decision-making. Experimental results obtained on several facial landmark detection and face classification data sets validate the effectiveness of the proposed method.

scholarly journals Semantic Graph Neural Network: A Conversion from Spam Email Classification to Graph Classification

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Weisen Pan ◽  
Jian Li ◽  
Lisa Gao ◽  
Liexiang Yue ◽  
Yan Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Classification Problem ◽  
Graph Classification ◽  
Method Performance ◽  
The Public ◽  
Semantic Graph ◽  
Public Dataset ◽  
Public Datasets ◽  
Better Than ◽  
Email Classification

In this study, we propose a method named Semantic Graph Neural Network (SGNN) to address the challenging task of email classification. This method converts the email classification problem into a graph classification problem by projecting email into a graph and applying the SGNN model for classification. The email features are generated from the semantic graph; hence, there is no need of embedding the words into a numerical vector representation. The method performance is tested on the different public datasets. Experiments in the public dataset show that the presented method achieves high accuracy in the email classification test against a few public datasets. The performance is better than the state-of-the-art deep learning-based method in terms of spam classification.

Heritage

2018 ◽  
Author(s):  
Omar Shaikh ◽  
Stefano Bonino
Keyword(s):  
Facial Expressions ◽  
South Asian ◽  
Online Video ◽  
The South ◽  
Men And Women ◽  
The Public ◽  
Video Footage ◽  
Community Heritage ◽  
Personal Accounts ◽  
The Way

The Colourful Heritage Project (CHP) is the first community heritage focused charitable initiative in Scotland aiming to preserve and to celebrate the contributions of early South Asian and Muslim migrants to Scotland. It has successfully collated a considerable number of oral stories to create an online video archive, providing first-hand accounts of the personal journeys and emotions of the arrival of the earliest generation of these migrants in Scotland and highlighting the inspiring lessons that can be learnt from them. The CHP’s aims are first to capture these stories, second to celebrate the community’s achievements, and third to inspire present and future South Asian, Muslim and Scottish generations. It is a community-led charitable project that has been actively documenting a collection of inspirational stories and personal accounts, uniquely told by the protagonists themselves, describing at first hand their stories and adventures. These range all the way from the time of partition itself to resettling in Pakistan, and then to their final accounts of arriving in Scotland. The video footage enables the public to see their facial expressions, feel their emotions and hear their voices, creating poignant memories of these great men and women, and helping to gain a better understanding of the South Asian and Muslim community’s earliest days in Scotland.

IMPACT OF GOVERNMENT DEBT ON FINANCIAL SECURITY OF UKRAINE

2017 ◽  
Author(s):  
Olena Pikaliuk ◽  
◽  
Dmitry Kovalenko ◽  
Keyword(s):  
Economic Development ◽  
Public Debt ◽  
Positive Impact ◽  
The State ◽  
Financial Security ◽  
State Budget ◽  
The Public ◽  
Debt Security ◽  
The Stability ◽  
The Impact

One of the main criteria for economic development is the size of the public debt and its dynamics. The article considers the impact of public debt on the financial security of Ukraine. The views of scientists on the essence of public debt and financial security of the state are substantiated. An analysis of the dynamics and structure of public debt of Ukraine for 2014-2019. It is proved that one of the main criteria for economic development is the size of public debt and its dynamics. State budget deficit, attracting and using loans to cover it have led to the formation and significant growth of public debt in Ukraine. The volume of public debt indicates an increase in the debt security of the state, which is a component of financial security. Therefore, the issue of the impact of public debt on the financial security of Ukraine is becoming increasingly relevant. The constant growth and large amounts of debt make it necessary to study it, which will have a positive impact on economic processes that will ensure the stability of the financial system and enhance its security.

ASSESSMENT OF PINE CROPS GROWTH IN BAIMAK FORESTRY

2020 ◽  
Vol 37 (3) ◽  
pp. 83-90
Author(s):  
T.Z. Mutallapov ◽  
Keyword(s):  
Scots Pine ◽  
Forest Ecosystem ◽  
Distribution Curve ◽  
Comparative Assessment ◽  
Tree Distribution ◽  
Entire Structure ◽  
Structure Changes ◽  
Large Trees ◽  
Different Types ◽  
The Stability

The article presents the results of evaluating the growth of Scots pine in the Baymak forest area. The analysis of forestry and taxation indicators of Scots pine crops on the studied sample areas is carried out, and a comparative assessment of the growth of forest crops growing in different types of forest is given. Increased competition in plantings leads to the natural decline of stunted trees, which is the result of differentiation in the stand. As a result, its structure changes, the number of large trees increases, and, accordingly, the stability of the forest ecosystem increases. In this regard, the appearance of the tree distribution curve by thickness levels also changes. It becomes more "flat", and its competitive load is more evenly distributed over the entire structure of the stand, and competition is weakened.

A Survey on Computational Methods for Essential Proteins and Genes Prediction

2019 ◽  
Vol 14 (3) ◽  
pp. 211-225 ◽  
Author(s):  
Ming Fang ◽  
Xiujuan Lei ◽  
Ling Guo
Keyword(s):  
Computational Methods ◽  
Drug Targets ◽  
Disease Genes ◽  
Essential Proteins ◽  
Experimental Identification ◽  
Cellular Life ◽  
Different Types ◽  
The Stability ◽  
Human Disease Genes ◽  
Biology Research

Background: Essential proteins play important roles in the survival or reproduction of an organism and support the stability of the system. Essential proteins are the minimum set of proteins absolutely required to maintain a living cell. The identification of essential proteins is a very important topic not only for a better comprehension of the minimal requirements for cellular life, but also for a more efficient discovery of the human disease genes and drug targets. Traditionally, as the experimental identification of essential proteins is complex, it usually requires great time and expense. With the cumulation of high-throughput experimental data, many computational methods that make useful complements to experimental methods have been proposed to identify essential proteins. In addition, the ability to rapidly and precisely identify essential proteins is of great significance for discovering disease genes and drug design, and has great potential for applications in basic and synthetic biology research. Objective: The aim of this paper is to provide a review on the identification of essential proteins and genes focusing on the current developments of different types of computational methods, point out some progress and limitations of existing methods, and the challenges and directions for further research are discussed.

Sign in / Sign up

Export Citation Format

Share Document