The capacity to estimate the head pose of another person is a common human ability that presents a unique challenge for computer vision systems. Compared to face detection and recognition, which have been the primary foci of face-related vision research, identity-invariant head pose estimation has fewer rigorously evaluated systems or generic solutions. In this paper, we discuss the inherent difficulties in head pose estimation and present an organized survey describing the evolution of the field. Our discussion focuses on the advantages and disadvantages of each approach and spans 90 of the most innovative and characteristic papers that have been published on this topic. We compare these systems by focusing on their ability to estimate coarse and fine head pose, highlighting approaches that are well suited for unconstrained environments.

Head Pose Estimation in Computer Vision: A Survey

Head pose and eye location for gaze estimation have been separately studied in numerous works in the literature. Previous research shows that satisfactory accuracy in head pose and eye location estimation can be achieved in constrained settings. However, in the presence of nonfrontal faces, eye locators are not adequate to accurately locate the center of the eyes. On the other hand, head pose estimation techniques are able to deal with these conditions; hence, they may be suited to enhance the accuracy of eye localization. Therefore, in this paper, a hybrid scheme is proposed to combine head pose and eye location information to obtain enhanced gaze estimation. To this end, the transformation matrix obtained from the head pose is used to normalize the eye regions, and in turn, the transformation matrix generated by the found eye location is used to correct the pose estimation procedure. The scheme is designed to enhance the accuracy of eye location estimations, particularly in low-resolution videos, to extend the operative range of the eye locators, and to improve the accuracy of the head pose tracker. These enhanced estimations are then combined to obtain a novel visual gaze estimation system, which uses both eye location and head information to refine the gaze estimates. From the experimental results, it can be derived that the proposed unified scheme improves the accuracy of eye estimations by 16% to 23%. Furthermore, it considerably extends its operating range by more than 15° by overcoming the problems introduced by extreme head poses. Moreover, the accuracy of the head pose tracker is improved by 12% to 24%. Finally, the experimentation on the proposed combined gaze estimation system shows that it is accurate (with a mean error between 2° and 5°) and that it can be used in cases where classic approaches would fail without imposing restraints on the position of the head.

/pdf/combining-head-pose-and-eye-location-information-for-gaze-11zez155ma.pdf

Combining Head Pose and Eye Location Information for Gaze Estimation

Automatic facial expression analysis is the most commonly studied aspect of behavior understanding and human-computer interface. The main difficulty with facial emotion recognition system is to implement general expression models. The same facial expression may vary differently across humans; this can be true even for the same person when the expression is displayed in different contexts. These factors present a significant challenge for the recognition task. The method we applied, which is reminiscent of the “baseline method”, utilizes dynamic dense appearance descriptors and statistical machine learning techniques. Histograms of oriented gradients (HoG) are used to extract the appearance features by accumulating the gradient magnitudes for a set of orientations in 1-D histograms defined over a size-adaptive dense grid, and Support Vector Machines with Radial Basis Function kernels are the base learners of emotions. The overall classification performance of the emotion detection reached 70% which is better than the 56% accuracy achieved by the “baseline method” presented by the challenge organizers.

Emotion recognition using dynamic grid-based HoG features

Head pose is an important vision cue for scene interpretation and human computer interaction. To determine the head pose, one may consider the low-dimensional manifold structure of the face view points in image space. In this paper, we present an appearance-based strategy for head pose estimation using supervised graph embedding (GE) analysis. Thinking globally and fitting locally, we first construct the neighborhood weighted graph in the sense of supervised LLE. The unified projection is calculated in a closed-form solution based on the GE linearization. We then project new data (face view images) into the embedded low-dimensional subspace with the identical projection. The head pose is finally estimated by the K-nearest neighbor classification. We test the proposed method on 18,100 USF face view images. Experimental results show that, even using a very small training set (e.g. 10 subjects), GE achieves higher head pose estimation accuracy with more efficient dimensionality reduction than the existing methods.

Graph embedded analysis for head pose estimation

This paper introduces a novel camera mouse driven by 3D model based visual face tracking technique. While camera becomes standard configuration for personal computer (PC) and computer speed becomes faster and faster, achieving human machine interaction through visual face tracking becomes a feasible solution to hand-free control. The human facial movement can be decomposed into rigid movement, e.g. rotation and translation, and non-rigid movement, such as the open/close of mouth, eyes, and facial expressions, etc. We introduce our visual face tracking system that can robustly and accurately retrieve these motion parameters from video at real-time. After calibration, the retrieved head orientation and translation can be employed to navigate the mouse cursor, and the detection of mouth movement can be utilized to trigger mouse events. 3 mouse control modes are investigated and compared. Experiments in Windows XP environment verify the convenience of navigation and operations using our face mouse. This technique can be an alternative input device for people with hand and speech disability and for futuristic vision-based game and interface.

http://www.ifp.illinois.edu/~jilintu/research/record/jilintu_mouse.pdf

Face as mouse through visual face tracking

A robust pose estimation approach is proposed by combining facial appearance asymmetry and 3D geometry in a coarse-to-fine framework. The rough face pose is first estimated by analyzing the asymmetry of the distribution of the facial component detection confidences on an image, which actually implies an intrinsic relation between the face pose and the facial appearance. Then, this rough face pose, as well as error bandwidth, is utilized into a 3D-to-2D geometrical model matching to refine the pose estimation. The proposed approach is able to track a face with fast motion in front of cluttered background and recover its pose robustly and accurately in real- time. Experiment results are provided to demonstrate its efficiency and accuracy.

/pdf/estimating-face-pose-by-facial-asymmetry-and-geometry-3n7n5l4it8.pdf

Estimating face pose by facial asymmetry and geometry

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Yi Zhou

Author Tools

Chat about Author

Papers

Estimating face pose by facial asymmetry and geometry