Face Tracking System Research Articles

Real-time face tracking and recognition using the mobile robots

The terrain is complex and dynamic in a disaster. This paper aimed at constructing a multipose-specific feature model for online face recognition and tracking in a search and rescue operation. This paper proposes an integrated multipose face tracking and recognition system mounted on an unmanned aerial vehicle (UAV). The face model is constructed online by multi-scale block local binary pattern (MB-LBP) for face recognition. The generic and specific face models are further integrated for face tracking. Mechanisms for the online update of face models to retrieve loss of face tracking are also implemented. The results show that the proposed system achieves stable and robust tracking despite uncertainties (e.g. non-rigid human face, face expression changes, different face poses, complex background, varying illumination, partial visual occlusion, and pose changes). The target loss during tracking can be retrieved correctly. In face recognition, the multipose-specific face model can achieve an accuracy (above 70%). The results demonstrate the feasibility of the proof-of-concept using UAV or ground mobile robot (GMR) for real-time face recognition and tracking.

Identification and Tracking of Facial Features

Abstract Facial feature points such as eyebrows, eyes, nose and mouth are prominent landmarks surrounding facial components. They depicts critical information about facial expression and head movement .Hence accurate and perfect detection of facial features is important in several applications like face recognition,face tracking,criminal identification,security and surveillance systems,Human computer interaction(HCI), model based video coding. This paper represents traditional methodologies and taxonomies along with brief introduction to recent approaches and advancements in the field proposed by various researchers that acts as a bottleneck under various situations like occlusion, pose variations, different lighting conditions.

Why, When, and How Much to Entertain Consumers in Advertisements? A Web-Based Facial Tracking Field Study

The presence of positive entertainment (e.g., visual imagery, upbeat music, humor) in TV advertisements can make them more attractive and persuasive. However, little is known about the downside of too much entertainment. This research focuses on why, when, and how much to entertain consumers in TV advertisements. We collected data in a large scale field study using 82 ads with various levels of entertainment shown to 178 consumers in their homes and workplaces. Using a novel web-based face tracking system, we continuously measure consumers' smile responses, viewing interest, and purchase intent. A simultaneous Bayesian hierarchical model is estimated to assess how different levels of entertainment affect purchases by endogenizing viewing interest. We find that entertainment has an inverted U-shape relationship to purchase intent. Importantly, we separate entertainment into that which comes before the brand versus that which comes after, and find that the latter is positively associated with purchase intent while the former is not.

An Application on Medical Tele-Diagnosis Robot (MTR) for Real-Time Motion Detection

The use of medical robots in healthcare industry especially in rural areas are hitting limelight these days. Development of Medical Tele-diagnosis Robot (MTR) has gain importance to unravel the need of medical emergencies. Nevertheless, challenges for a better visual communication still arises. Thus, a face identification and tracking system for MTR is designed to allow an automated visual which will ease the medical specialist to identify and keep the patient in the best view for visual communication. This paper emphasis on the motion detection module which is the first module of the system. An improved motion detection technique is proposed which suits a real-time application for a dynamic background. Frame differencing method was used to detect the motion of the target. The developed motion detection module succeeded an accuracy of 96% resulting an average of 97% of the whole MTR.

Combination of Multiple Measurement Cues for Visual Face Tracking

Visual face tracking is an important building block for all intelligent living and working spaces, as it is able to locate persons without any human intervention or the need for the users to carry sensors on themselves. In this paper we present a novel face tracking system built on a particle filtering framework that facilitates the use of non-linear visual measurements on the facial area. We concentrate on three different such non-linear visual measurement cues, namely object detection, foreground segmentation and colour matching. We derive robust measurement likelihoods under a unified representation scheme and fuse them into our face tracking algorithm. This algorithm is complemented with optimum selection of the particle filter's object model and a target handling scheme. The resulting face tracking system is extensively evaluated and compared to baseline ones.

Construction of a Video Dataset for Face Tracking Benchmarking Using a Ground Truth Generation Tool

In the current generation of smart mobile devices, object tracking is one of the most important research topics for computer vision. Because human face tracking can be widely used for many applications, collecting a dataset of face videos is necessary for evaluating the performance of a tracker and for comparing different approaches. Unfortunately, the well-known benchmark datasets of face videos are not sufficiently diverse. As a result, it is difficult to compare the accuracy between different tracking algorithms in various conditions, namely illumination, background complexity, and subject movement. In this paper, we propose a new dataset that includes 91 face video clips that were recorded in different conditions. We also provide a semi-automatic ground-truth generation tool that can easily be used to evaluate the performance of face tracking systems. This tool helps to maintain the consistency of the definitions for the ground-truth in each frame. The resulting video data set is used to evaluate well-known approaches and test their efficiency.

An Efficient Face Tracker Using Active Shape Model

To perform tracking in an illumination insensitive feature space, called the gradient logarithm field (GLF) feature space. The GLF feature mainly depends on the intrinsic characteristics of a face and is only marginally affected by the lighting source. The GLF feature is a global feature and does not depend on a specific face model, and thus is effective in tracking low-resolution faces. By using the GLF face tracking system, the modified image like editing image did not recognition. To overcome this problem, we propose, to find the original image of the editable image by using the outline dimension of the image. To recognition original face from the modified face, we track the inclusive(complete) region of the face. The Active Shape Model is used to recover the shape of the face and then find the face after comparing the faces.

The Optimizion of the Training Process Based on Adaboost Algorithm

The research started from the adaboost algorithm, which was optimized from the training process and the detection process, and on this basis face detection and tracking system was developed.Adaboost is a main algorithm in using.It calculate easily and extraction rate rapidly.But it also have some problem to resolve,such as the detection time is too long,the samples number is so limited and so on.This article give some optimizition methods in the training procedure.

Microphone array with computer vision based directivity

In the field of acoustic signal processing, one of the most popular areas of research is that of microphone arrays. As a small research project in this field, a microphone array system with adjustable polar response will be developed. To make research more innovative and multi-disciplinary, the polar response of the array will be controlled by a facial tracking system implemented with computer vision techniques. This research will take part as a means of combining coursework and research for courses in engineering acoustics, computer vision, and real-time digital signal processing at the University of Miami. Before any physical testing, simulations will be performed in Matlab to determine optimum array configurations and to analyze various facial tracking and dsp algorithms. Then, using a Texas Instruments TMS320C6713 DSP on a DSK audio target board, and writing code in C, Assembly, and Matlab, physical tests will be conducted to analyze performance of design. A MEMS microphone model will likely be used for its small size and low cost. A Microsoft Kinect and compatible desktop computer will be used for the computer vision interface. From there, results will be analyzed and further research in the area proposed.

A face-tracking system to detect falls in the elderly

An automated surveillance method that uses multiple image processing can detect, analyze, and track movements to identify emergency situations.

Research on the Real-Time Multiple Face Detection, Tracking and Recognition Based on Video

The video images of human face tracking and recognition is a hot research field of biometric recognition and artificial intelligence in recent years. This paper presents an automatic face tracking and recognition system, which can track multiple faces real-timely and recognize the identity. Aiming at Adaboost face detection algorithm is easy to false detection, presents a fusion algorithm based on Adaboost face detection algorithm and Active Shape Model. The algorithm is not only detect face real-timely but also remove the non-face areas; A multi thread CamShift tracking algorithm is proposed for many faces interlaced and face number of changes in the scene . Meanwhile, the algorithm also can identify the faces which have been tracked in the video. The experiment results show that the system is capable of improving the accurate rate of faces detection and recognition in complex backgrounds, and furthermore it also can track the real-time faces effectively.

500-fps face tracking system

In this paper, we propose a high-speed vision system that can be applied to real-time face tracking at 500 fps using GPU acceleration of a boosting-based face tracking algorithm. By assuming a small image displacement between frames, which is a property of high-frame rate vision, we develop an improved boosting-based face tracking algorithm for fast face tracking by enhancing the Viola---Jones face detector. In the improved algorithm, face detection can be efficiently accelerated by reducing the number of window searches for Haar-like features, and the tracked face pattern can be localized pixel-wise even when the window is sparsely scanned for a larger face pattern by introducing skin color extraction in the boosting-based face detector. The improved boosting-based face tracking algorithm is implemented on a GPU-based high-speed vision platform, and face tracking can be executed in real time at 500 fps for an 8-bit color image of 512 × 512 pixels. In order to verify the effectiveness of the developed face tracking system, we install it on a two-axis mechanical active vision system and perform several experiments for tracking face patterns.

Multiple Faces Tracking Based on Relevance Vector Machine

A multiple faces tracking system was presented based on Relevance Vector Machine (RVM) and Boosting learning. At the first frame, a face detector based on AdaBoost is used to detect faces, and the face motion models and face color models are created. The face motion model consists of a set of RVMs that learn the relationship between the motion of the face and its appearance in the image, and the face color model is the 2D histogram of the face region in CrCb color space. In the tracking process, different tracking methods are used according to different states of the faces and the states are changed according to the tracking results. When the full image search condition is satisfied, a full image search is started in order to find new coming faces and former occluded faces.

Video Face Tracking and Recognition with Skin Region Extraction and Deformable Template Matching

The authors describe a face tracking and recognition system for video indexing that handles variable face poses (left-right and up-down) and deformations due to speech and facial expressions. The system is based on deformable template matching, and employs person-specific templates at near-frontal poses for recognition, and novel person-independent templates at multiple poses on the view-sphere for tracking. Relative to an earlier version that used multiple person-specific templates at multiple (left-right) poses, the new system speeds up processing by (i) restricting attention to skin-color regions; (ii) performing recognition using the person-specific templates at near-frontal poses only; and (iii) tracking at non-frontal poses using the novel person-independent templates. Registration is also simplified since multiple views of each target individual are no longer required, but at the cost of a loss of recognition functionality at poses far from frontal (the system instead “remembers” the identity of each individual from near-frontal matches and tracks between them).

An Integrated Face Tracking and Facial Expression Recognition System

This article proposes a feature extraction method for an integrated face tracking and facial expression recognition in real time video. The method proposed by Viola and Jones [1] is used to detect the face region in the first frame of the video. A rectangular bounding box is fitted over for the face region and the detected face is tracked in the successive frames using the cascaded Support vector machine (SVM) and cascaded Radial basis function neural network (RBFNN). The haar-like features are extracted from the detected face region and they are used to create a cascaded SVM and RBFNN classifiers. Each stage of the SVM classifier and RBFNN classifier rejects the non-face regions and pass the face regions to the next stage in the cascade thereby efficiently tracking the face. The performance of tracking is evaluated using one hour video data. The performance of the cascaded SVM is compared with the cascaded RBFNN. The experiment results show that the proposed cascaded SVM classifier method gives better performance over the RBFNN and also the methods described in the literature using single SVM classifier [2]. While the face is being tracked, features are extracted from the mouth region for expression recognition. The features are modelled using a multi-class SVM. The SVM finds an optimal hyperplane to distinguish different facial expressions with an accuracy of 96.0%.

Tracking and Recognizing Multiple Faces Using Kalman Filter and ModularPCA

Real-time tracking and recognizing multiple faces in complex environments has the ability to provide e_cient security automation to large areas. Previous research has shown that Kalman filter techniques paired with the traditional face detection methods can be used to track one or more faces in a viewing region, but prove unreliable under variant conditions due to the inability to reliably distinguish between multiple trackers. A real-time face tracking and recognition system is presented that is capable of processing multiple faces simultaneously. The proposed system utilizes the Kalman filter for tracking and uses a low-level recognition system to properly distinguish between the many trackers. This low-level system is created using a face database of twenty unrelated people trained using Modular Principal Component Analysis (MPCA) and classification is performed using a feature correlation metric. After tracking the faces, they are then analyzed by a high-level face recognition subspace which is created using a large database of people and processed using Adaptive MPCA. The overall system is shown to provide reliable tracking of more than one person and to allow a more accurate recognition rate due to the ability to create a time-average of the recognized faces.

Cluster-Based Distributed Face Tracking in Camera Networks

In this paper, we present a distributed multicamera face tracking system suitable for large wired camera networks. Unlike previous multicamera face tracking systems, our system does not require a central server to coordinate the entire tracking effort. Instead, an efficient camera clustering protocol is used to dynamically form groups of cameras for in-network tracking of individual faces. The clustering protocol includes cluster propagation mechanisms that allow the computational load of face tracking to be transferred to different cameras as the target objects move. Furthermore, the dynamic election of cluster leaders provides robustness against system failures. Our experimental results show that our cluster-based distributed face tracker is capable of accurately tracking multiple faces in real-time. The overall performance of the distributed system is comparable to that of a centralized face tracker, while presenting the advantages of scalability and robustness.

Integrated detection and tracking of multiple faces using particle filtering and optical flow-based elastic matching

The design and implementation of a multiple face tracking framework that integrates face detection and face tracking is presented. Specifically, the incorporation of a novel proposal distribution and object shape model within the face tracking framework is proposed. A general solution that incorporates the most recent observation in the proposal distribution using a multiscale elastic matching-based optical flow algorithm is proposed. The proposed multiscale elastic matching-based optical flow algorithm is shown to be general and powerful in three significant ways. First, it allows for the tracking of both, rigid and elastic objects. Second, it enables robust tracking even in the face of sudden and gradual changes in illumination, scale and viewpoint. Third, it is suitable for tracking using both, fixed cameras and moving cameras. The proposed object shape model is based on a kernel-based line segment matching algorithm, which incorporates a voting scheme similar to the Radon Transform. The incorporation of the object shape model is shown to improve the computational complexity and accuracy of the face tracking algorithm and also enhance its robustness to occlusion, noise and scene clutter. Efficient techniques for particle sampling based on the Genetic Algorithm and for computation of the region-based likelihood function using the integral image are proposed. The incorporation of face detection within the face tracking algorithm is also proposed. Experimental results show that the proposed face tracking system is very robust in its ability to handle occlusion, noise, scene clutter and changes in illumination, scale and viewpoint and is also computationally efficient.

Efficient multiple faces tracking based on Relevance Vector Machine and Boosting learning

A multiple faces tracking system was presented based on Relevance Vector Machine (RVM) and Boosting learning. In this system, a face detector based on Boosting learning is used to detect faces at the first frame, and the face motion model and color model are created. The face motion model consists of a set of RVMs that learn the relationship between the motion of the face and its appearance, and the face color model is the 2D histogram of the face region in CrCb color space. In the tracking process different tracking methods (RVM tracking, local search, giving up tracking) are used according to different states of faces, and the states are changed according to the tracking results. When the full image search condition is satisfied, a full image search is started in order to find new coming faces and former occluded faces. In the full image search and local search, the similarity matrix is introduced to help matching faces efficiently. Experimental results demonstrate that this system can (a) automatically find new coming faces; (b) recover from occlusion, for example, if the faces are occluded by others and reappear or leave the scene and return; (c) run with a high computation efficiency, run at about 20 frames/s.

Face as mouse through visual face tracking

This paper introduces a novel camera mouse driven by visual face tracking based on a 3D model. As the camera becomes standard configuration for personal computers (PCs) and computation speed increases, achieving human–machine interaction through visual face tracking becomes a feasible solution to hands-free control. Human facial movements can be broken down into rigid motions, such as rotation and translation, and non-rigid motions such as opening, closing, and stretching of the mouth. First, we describe our face tracking system which can robustly and accurately retrieve these motion parameters from videos in real time [H. Tao, T. Huang, Explanation-based facial motion tracking using a piecewise Bezier volume deformation model, in: Proceedings of IEEE Computer Vision and Pattern Recogintion, vol. 1, 1999, pp. 611–617]. The retrieved (rigid) motion parameters can be employed to navigate the mouse cursor; the detection of mouth (non-rigid) motions triggers mouse events in the operating system. Three mouse control modes are investigated and their usability is compared. Experiments in the Windows XP environment verify the convenience of our camera mouse in hands-free control. This technology can be an alternative input option for people with hand and speech disability, as well as for futuristic vision-based games and interfaces.