Head Pose Estimation in Computer Vision: A Survey

By Erik Murphy-Chutorian, Mohan Manubhai Trivedi. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009

1,407 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/cf87e167446bd22e9422445dfed4f74a3f0579f9

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In Computer Vision and Pattern Recognition

Citations in the corpus

Facial Landmark Detection: A Literature Survey (2018, background): “For example, facial expression recognition [68] and head pose estimation algorithms [66] may heavily rely on the facial shape information provided by the landmark locations.”
"Here's looking at you, kid". Detecting people looking at each other in videos (2011, background): “A survey of head pose estimation is given in [19].”
FSA-Net: Learning Fine-Grained Structure Aggregation for Head Pose Estimation From a Single Image (2019, methodology): “On the other hand, it is also very popular to adopt different modalities to compensate for the loss of information [14, 27, 9, 29].”
Head pose estimation via probabilistic high-dimensional regression (2015, background): “For example, in social event analysis, 3D head-pose information drastically helps to determine the interaction between people and to extract the visual focus of attention [4].”
EgoCom: A Multi-Person Multi-Modal Egocentric Communications Dataset (2020, methodology): “Similarly, EgoCom enhances visual cues through the egocentric perspective by enabling spatial AI techniques like head-pose estimation combined with traditional computer vision techniques like body pose estimation (Murphy-Chutorian and Trivedi, 2009).”
3D Constrained Local Model for rigid and non-rigid facial tracking (2012, methodology): “Static methods are more robust while dynamic ones show better overall accuracy, but are prone to failure during longer tracking sequences due to accumulation of error [20].”
Learning about Canonical Views from Internet Image Collections (2012, background): “This problem, called pose estimation, is widely studied in computer vision (see [19] for a recent survey for the special case of head poses) and is quite difficult.”
3D Social Saliency from Head-mounted Cameras (2012, background): “, an outside camera looking into a scene, state-of-the-art face pose estimation algorithms cannot produce reliable face orientation and location estimation beyond approximately 45 degrees of a head facing the camera directly [6].”
Computer Vision and Image Understanding (2016, methodology): “There has been a plethora of methods proposed in literature to solve this problem ( Murphy-Chutorian and Trivedi, 2009 ).”
No Matter Where You Are: Flexible Graph-Guided Multi-task Learning for Multi-view Head Pose Classification under Target Motion (2013, background): “Head pose estimation and tracking is critical for surveillance and human-behavior understanding, and has been extensively studied for over a decade [15].”
Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization (2011, background): “For instance, an efficient estimate of the head pose can be obtained [21] with only some landmarks.”
OpenFace: An open source facial behavior analysis toolkit (2016, background): “A full review of work in facial landmark detection, head pose, eye gaze, and action unit estimation is outside the scope of this paper, we refer the reader to recent reviews of the field [17, 18, 30, 46, 51, 61].”
Sign Language Recognition (2011, background): “[74] Little work has currently been performed on body pose, which plays a part during dialogues and stories.”
Combining Head Pose and Eye Location Information for Gaze Estimation (2012, background): “topics separately; recent surveys on eye center location and head pose estimation can be found in [17] and [31].”
Face detection, pose estimation, and landmark localization in the wild (2012, background): “Space does not allow for a full review; we refer the reader to the recent surveys [41, 26, 39].”
Cumulative Attribute Space for Age and Crowd Density Estimation (2013, background): “Examples of such problems include age estimation from facial images [10, 12, 15, 16, 33, 35], crowd counting [4, 5, 8, 25], and human body/face pose (view angle) estimation [14, 27, 34].”
3D Head Pose Estimation for TV Setups (2013, methodology): “More information on these methods can be found in [18].”
Face Alignment in Full Pose Range: A 3D Total Solution (2015, methodology): “Face rotation is traditionally formulated with the Euler angles [55] including pitch, yaw and roll.”
Head pose estimation in the wild using Convolutional Neural Networks and adaptive gradient methods (2017, positive): “A complete description of all the methods available is out of the scope of this article so we refer the reader to a recent survey [5].”