Tracking Facial Features Research Articles

Drowsy Driver Detection System

Abstract: Drowsy driver detection system is a safety technology designed to identify the potential signs of fatigue or drowsiness of the driver to prevent accidents caused as a result of not being alert. It is a significant risk factor for traffic accidents which has led to thousands of accidents, injuries, and fatalities each year. In this system we have used some techniques to monitor the driver’s behavior or state. This involves tracking facial features, eye movements or blink patterns and yawning. The system identifies signs of fatigue, such as slow eyelid closure, increased blink duration and yawn which often indicate drowsiness. As technology advances, these systems are becoming more accurate, efficient and integrated with other vehicle safety features. The continued development of drowsy driver detection technology holds significant promise for reducing traffic accidents and improving overall road safety.

Usability Evaluation of Facial Emotion Recognition System

Face detection can identify and verify faces from images, videos, and other forms of face graphics. It tracks facial features, contours, and textures to analyze individuals’ unique biometrics and demographic details. Humans can express thousands of facial expressions, all of which vary in intensity, complexity, and meaning. Facial emotion recognition is a type of facial recognition system, and it is a technology that can detect human emotions through facial expressions. In the past few decades, real-time facial emotion recognition systems have been an active area of research. Due to the Covid-19 pandemic, most learning methods have changed from physical learning to online learning. Therefore, it is difficult for teachers to understand students' emotions in the teaching process, and some emotions constitute obstacles to students' classroom participation and test scores. Nowadays, as students, we also know that it is difficult for lecturers to capture the emotions of students through the online learning mode and change the learning mode according to the emotions of the students. As a result, most of the time students feel bored and cannot concentrate in class because they are not interested in the lecturer's learning mode. Therefore, the learning materials prepared by the lecturer will become invalid due to the inattention of students in class. In this project, we will use a facial landmark to detect real-time facial expressions via a webcam. The purpose of the project is to establish a facial emotion recognition system to recognize the emotions of students in class and view the report of the emotions of the students. The result of this project is to allow lecturers and students to better understand their emotions during class. By doing so, we can recognize emotions and classify learners’ participation and interest in topics that are mapped as feedback to the lecturer to improve the learner’s experience. In addition, after students use the system, there will be emotional results. For example, students can learn about their emotional changes during class hours.

RETRACTED: Unity 3D animation modeling based on machine vision and embedded system

It proposes a reliable method for estimating a single image, the three-dimensional pose of the human head. As opposed to different examinations, this technique utilizes data about just the skin and hair regions. To begin with, utilize an outwardly uniform shading framework and Neural Network (NN) to extricate the skin and hair territories, and afterward, it utilizes a productive calculation to recognize the appearances in the picture. The focal point of the region is then used to ascertain in any event the inertial tomahawks of both the skin and hair territories and to gage the three-dimensional stance of the head. It tried our technique by assessing the three-dimensional stance of the head from a live video grouping. It is face detection and recognition recognizes, presents a complete real-time face recognition system, and uses an FPGA sampling module. The robustness and reliability of this technique are significantly improved compared to those based on tracking facial features. This is because extract such stability and use global information as areas, centers, and other related skin and hair areas that can be insensitive to local changes with facial features. It utilizes an exceptionally incredible skin and hair zone recognition calculation contrasted with other announced strategies utilized because our stance assessment calculation doesn't utilize any 3D head. Likewise, this technique is more dependable and simpler to use for skin areas model and camera parameters.

Driver Gaze Tracking and Eyes Off the Road Detection System

Distracted driving is one of the main causes of vehicle collisions in the United States. Passively monitoring a driver's activities constitutes the basis of an automobile safety system that can potentially reduce the number of accidents by estimating the driver's focus of attention. This paper proposes an inexpensive vision-based system to accurately detect Eyes Off the Road (EOR). The system has three main components: 1) robust facial feature tracking; 2) head pose and gaze estimation; and 3) 3-D geometric reasoning to detect EOR. From the video stream of a camera installed on the steering wheel column, our system tracks facial features from the driver's face. Using the tracked landmarks and a 3-D face model, the system computes head pose and gaze direction. The head pose estimation algorithm is robust to nonrigid face deformations due to changes in expressions. Finally, using a 3-D geometric analysis, the system reliably detects EOR.

Continuous Head Movement Estimator for Driver Assistance: Issues, Algorithms, and On-Road Evaluations

Analysis of a driver's head behavior is an integral part of a driver monitoring system. In particular, the head pose and dynamics are strong indicators of a driver's focus of attention. Many existing state-of-the-art head dynamic analyzers are, however, limited to single-camera perspectives, which are susceptible to occlusion of facial features from spatially large head movements away from the frontal pose. Nonfrontal glances away from the road ahead, however, are of special interest since interesting events, which are critical to driver safety, occur during those times. In this paper, we present a distributed camera framework for head movement analysis, with emphasis on the ability to robustly and continuously operate even during large head movements. The proposed system tracks facial features and analyzes their geometric configuration to estimate the head pose using a 3-D model. We present two such solutions that additionally exploit the constraints that are present in a driving context and video data to improve tracking accuracy and computation time. Furthermore, we conduct a thorough comparative study with different camera configurations. For experimental evaluations, we collected a novel head pose data set from naturalistic on-road driving in urban streets and freeways, with particular emphasis on events inducing spatially large head movements (e.g., merge and lane change). Our analyses show promising results.

Robust facial feature tracking under varying face pose and facial expression

This paper presents a hierarchical multi-state pose-dependent approach for facial feature detection and tracking under varying facial expression and face pose. For effective and efficient representation of feature points, a hybrid representation that integrates Gabor wavelets and gray-level profiles is proposed. To model the spatial relations among feature points, a hierarchical statistical face shape model is proposed to characterize both the global shape of human face and the local structural details of each facial component. Furthermore, multi-state local shape models are introduced to deal with shape variations of some facial components under different facial expressions. During detection and tracking, both facial component states and feature point positions, constrained by the hierarchical face shape model, are dynamically estimated using a switching hypothesized measurements (SHM) model. Experimental results demonstrate that the proposed method accurately and robustly tracks facial features in real time under different facial expressions and face poses.

Tracking facial features with occlusions

Facial expression recognition consists of determining what kind of emotional content is presented in a human face. The problem presents a complex area for exploration, since it encompasses face acquisition, facial feature tracking, facial expression classification. Facial feature tracking is of the most interest. Active Appearance Model (AAM) enables accurate tracking of facial features in real-time, but lacks occlusions and self-occlusions. In this paper we propose a solution to improve the accuracy of fitting technique. The idea is to include occluded images into AAM training data. We demonstrate the results by running experiments using gradient descent algorithm for fitting the AAM. Our experiments show that using fitting algorithm with occluded training data improves the fitting quality of the algorithm.

Manifold based analysis of facial expression

We propose a novel approach for modeling, tracking, and recognizing facial expressions on a low-dimensional expression manifold. A modified Lipschitz embedding is developed to embed aligned facial features in a low-dimensional space, while keeping the main structure of the manifold. In the embedded space, a complete expression sequence becomes a path on the expression manifold, emanating from a center that corresponds to the neutral expression. As an offline training stage, facial contour features are first clustered in this space, using a mixture model. For each cluster in the low-dimensional space, a specific ASM model is learned, in order to avoid incorrect matching due to non-linear image variations. A probabilistic model of transitions between the clusters and paths in the embedded space is then learned. Given a new expression sequence, we use ICondensation to track facial features, while recognizing facial expressions simultaneously, within the common probabilistic framework. Experimental results demonstrate that our probabilistic facial expression model on the manifold significantly improves facial deformation tracking and expression recognition. We also synthesize image sequences of changing expressions through the manifold model.

Face verification through tracking facial features.

We propose an algorithm for face verification through tracking facial features by using sequential importance sampling. Specifically, we first formulate tracking as a Bayesian inference problem and propose to use Markov chain Monte Carlo techniques for obtaining an empirical solution. A reparameterization is introduced under parametric motion assumption, which facilitates the empirical estimation and also allows verification to be addressed along with tracking. The facial features to be tracked are defined on a grid with Gabor attributes (jets). The motion of facial feature points is modeled as a global two-dimensional (2-D) affine transformation (accounting for head motion) plus a local deformation (accounting for residual motion that is due to inaccuracies in 2-D affine modeling and other factors such as facial expression). Motion of both types is processed simultaneously by the tracker: The global motion is estimated by importance sampling, and the residual motion is handled by incorporating local deformation into the measurement likelihood in computing the weight of a sample. Experiments with a real database of face image sequences are presented.