Recent advances in visual and infrared face recognition: a review
Computer Vision and Image Understanding
Imaging Facial Physiology for the Detection of Deceit
International Journal of Computer Vision
Multi-view face and eye detection using discriminant features
Computer Vision and Image Understanding
Saliency model-based face segmentation and tracking in head-and-shoulder video sequences
Journal of Visual Communication and Image Representation
Impact of Gaze Analysis on the Design of a Caption Production Software
UAHCI '09 Proceedings of the 5th International Conference on Universal Access in Human-Computer Interaction. Part III: Applications and Services
Real-Time Probabilistic Tracking of Faces in Video
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Tracking a detected face with dynamic programming
Image and Vision Computing
SPPRA '08 Proceedings of the Fifth IASTED International Conference on Signal Processing, Pattern Recognition and Applications
Face tracking and recognition considering the camera's field of view
HBU'10 Proceedings of the First international conference on Human behavior understanding
Efficient model-based linear head motion recovery from movies
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Video-object segmentation and 3D-trajectory estimation for monocular video sequences
Image and Vision Computing
Person spotting: video shot retrieval for face sets
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
A hierarchical system for recognition, tracking and pose estimation
MLMI'04 Proceedings of the First international conference on Machine Learning for Multimodal Interaction
Probabilistic detection and tracking of faces in video
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Classifier combination for face localization in color images
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Robust head tracking with particles based on multiple cues fusion
ECCV'06 Proceedings of the 2006 international conference on Computer Vision in Human-Computer Interaction
FaceMouse: a human-computer interface for tetraplegic people
ECCV'06 Proceedings of the 2006 international conference on Computer Vision in Human-Computer Interaction
A cascade face recognition system using hybrid feature extraction
Digital Signal Processing
Tracking-by-detection of multiple persons by a resample-move particle filter
Machine Vision and Applications
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This paper presents a new probabilistic method for detecting and tracking multiple faces in a video sequence. The proposed method integrates the information of face probabilities provided by the detector and the temporal information provided by the tracker to produce a method superior to the available detection and tracking methods. The three novel contributions of the paper are: 1) Accumulation of probabilities of detection over a sequence. This leads to coherent detection over time and, thus, improves detection results. 2) Prediction of the detection parameters which are position, scale, and pose. This guarantees the accuracy of accumulation as well as a continuous detection. 3) The representation of pose is based on the combination of two detectors, one for frontal views and one for profiles. Face detection is fully automatic and is based on the method developed by Schneiderman and Kanade (2000). It uses local histograms of wavelet coefficients represented with respect to a coordinate frame fixed to the object. A probability of detection is obtained for each image position and at several scales and poses. The probabilities of detection are propagated over time using a Condensation filter and factored sampling. Prediction is based on a zero order model for position, scale, and pose; update uses the probability maps produced by the detection routine. The proposed method can handle multiple faces, appearing/disappearing faces as well as changing scale and pose. Experiments carried out on a large number of sequences taken from commercial movies and the Web show a clear improvement over the results of frame-based detection (in which the detector is applied to each frame of the video sequence).