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114
Detecting faces in images: A survey
- IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2002
"... Images containing faces are essential to intelligent vision-based human computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation, and expression recognition. However, many reported methods assume that the faces in an image or an image se ..."
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Cited by 839 (4 self)
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Images containing faces are essential to intelligent vision-based human computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation, and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face regardless of its three-dimensional position, orientation, and the lighting conditions. Such a problem is challenging because faces are nonrigid and have a high degree of variability in size, shape, color, and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics, and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for future research.
Ensemble Tracking
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2007
"... We consider tracking as a binary classification problem, where an ensemble of weak classifiers is trained on-line to distinguish between the object and the background. The ensemble of weak classifiers is combined into a strong classifier using AdaBoost. The strong classifier is then used to label pi ..."
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Cited by 328 (2 self)
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We consider tracking as a binary classification problem, where an ensemble of weak classifiers is trained on-line to distinguish between the object and the background. The ensemble of weak classifiers is combined into a strong classifier using AdaBoost. The strong classifier is then used to label pixels in the next frame as either belonging to the object or the background, giving a confidence map. The peak of the map, and hence the new position of the object, is found using mean shift. Temporal coherence is maintained by updating the ensemble with new weak classifiers that are trained on-line during tracking. We show a realization of this method and demonstrate it on several video sequences. 1
Q.: In the eye of the beholder: A survey of models for eyes and gaze.
- IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI)
, 2010
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Towards Robust Multi-cue Integration for Visual Tracking
- Machine Vision and Applications
, 2001
"... Even though many of today's vision algorithms are very successful, they lack robustness since they are typically limited to a particular situation. In this paper we argue that the principles of sensor and model integration can increase the robustness of today's computer vision systems ..."
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Cited by 80 (1 self)
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Even though many of today's vision algorithms are very successful, they lack robustness since they are typically limited to a particular situation. In this paper we argue that the principles of sensor and model integration can increase the robustness of today's computer vision systems substantially. As an example multi-cue tracking of faces is discussed. The approach is based on the principles of self-organization of the integration mechanism and self-adaptation of the cue models during tracking. Experiments show that the robustness of simple models is leveraged significantly by sensor and model integration.
Fusion of Multiple Tracking Algorithms for Robust People Tracking
- In Proc. of ECCV’02
, 2002
"... This paper shows how the output of a number of detection and tracking algorithms can be fused to achieve robust tracking of people in an indoor environment. The new tracking system contains three co-operating parts: i) an Active Shape Tracker using a PCA-generated model of pedestrian outline shapes, ..."
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Cited by 68 (6 self)
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This paper shows how the output of a number of detection and tracking algorithms can be fused to achieve robust tracking of people in an indoor environment. The new tracking system contains three co-operating parts: i) an Active Shape Tracker using a PCA-generated model of pedestrian outline shapes, ii) a Region Tracker, featuring region splitting and merging for multiple hypothesis matching, and iii) a Head Detector to aid in the initialisation of tracks. Data from the three parts are fused together to select the best tracking hypotheses. The new method is validated using sequences from surveillance cameras in a underground station. It is demonstrated that robust realtime tracking of people can be achieved with the new tracking system using standard PC hardware.
Robust Face Tracking using Color
- 4th IEEE International Conference on Automatic Face and Gesture Recognition
, 2000
"... In this paper we discuss a new robust tracking technique applied to histograms of intensity normalized color. This technique supports a video codec based on orthonormal basis coding. Orthonormal basis coding can be very efficient when the images to be coded have been normalized in size and position. ..."
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Cited by 66 (17 self)
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In this paper we discuss a new robust tracking technique applied to histograms of intensity normalized color. This technique supports a video codec based on orthonormal basis coding. Orthonormal basis coding can be very efficient when the images to be coded have been normalized in size and position. However, an imprecise tracking procedure can have a negative impact on the efficiency and the quality of reconstruction of this technique, since it may increase the size of the required basis space. The face tracking procedure described in this paper has certain advantages, such as greater stability, higher precision, and less jitter, over conventional tracking techniques using color histograms. In addition to those advantages, the features of the tracked object such as mean and variance are mathematically describable.
Comprehensive Colour Image Normalization
, 1998
"... . The same scene viewed under two different illuminants induces two different colour images. If the two illuminants are the same colour but are placed at different positions then corresponding rgb pixels are related by simple scale factors. In contrast if the lighting geometry is held fixed but the ..."
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Cited by 63 (6 self)
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. The same scene viewed under two different illuminants induces two different colour images. If the two illuminants are the same colour but are placed at different positions then corresponding rgb pixels are related by simple scale factors. In contrast if the lighting geometry is held fixed but the colour of the light changes then it is the individual colour channels (e.g. all the red pixel values or all the green pixels) that are a scaling apart. It is well known that the image dependencies due to lighting geometry and illuminant colour can be respectively removed by normalizing the magnitude of the rgb pixel triplets (e.g. by calculating chromaticities) and by normalizing the lengths of each colour channel (by running the `grey-world' colour constancy algorithm). However, neither normalization suffices to account for changes in both the lighting geometry and illuminant colour. In this paper we present a new comprehensive image normalization which removes image dependency on lighting...
Recognizing hand gestures using motion trajectories
- IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Fort Collins, CO
, 1999
"... We present an algorithm for extracting and classifying two-dimensional motion in an image sequence based on motion trajectories. First, a multiscale segmentation is performed to generate homogeneous regions in each frame. Regions between consecutive frames are then matched to obtain 2-view correspon ..."
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Cited by 55 (0 self)
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We present an algorithm for extracting and classifying two-dimensional motion in an image sequence based on motion trajectories. First, a multiscale segmentation is performed to generate homogeneous regions in each frame. Regions between consecutive frames are then matched to obtain 2-view correspondences. Afine transformations are computed from each pair of corresponding regions to define pixel matches. Pixels matches over consecutive images pairs are concatenated to obtain pixel-level motion trajectories across the image sequence. Motion patterns are learned from the extracted trajectories using a timedelay neural network. We apply the proposed method to recognize 40 hand gestures of American Sign Language. Experimental results show that motion patterns in hand gestures can be extracted and recognized with high recognition rate using motion trajectories. 1
Things That See
- Communications of the ACM
, 2000
"... nvergence and ubiquity. At the same time, inexpensive computing power is enabling a quiet revolution in the machine perception of human action. In the near future, we expect machine perception to converge with ubiquitous computing and communication. Exploring machine vision for human-computer inter ..."
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Cited by 54 (4 self)
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nvergence and ubiquity. At the same time, inexpensive computing power is enabling a quiet revolution in the machine perception of human action. In the near future, we expect machine perception to converge with ubiquitous computing and communication. Exploring machine vision for human-computer interaction. THINGS THAT SEE COMMUNICA 0 A OF THE AE March 2000/V4 43, No. 3 55 PUI Figure 1. Interacting with the Magic Board (iihm.imag.fr/demos/magicboard/). Physical whiteboard Workstation Video projector Video camera (a) The apparatus of the Magic Board; (b) Selecting a physical drawing with the finger; (c) Copying the selected drawing; (d) Completing the drawing with physical markers; (e) The menu at the top of the physical board to facilitate reinitialization. a c b d e What Can Machine Vision Do For You? Machine vision is the observation of an environment using cameras. It differs from image
Detecting human faces in color images
, 1999
"... A method for detecting human faces in color images is described that first separates skin regions from nonskin regions and then locates faces within skin regions. A chroma chart is prepared via a training process that contains the likelihoods of different colors representing the skin. Using the chro ..."
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Cited by 53 (0 self)
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A method for detecting human faces in color images is described that first separates skin regions from nonskin regions and then locates faces within skin regions. A chroma chart is prepared via a training process that contains the likelihoods of different colors representing the skin. Using the chroma chart, a color image is transformed into a gray scale image in such a way that the gray value at a pixel shows the likelihood of the pixel representing the skin. An obtained gray scale image is then segmented to skin and nonskin regions, and model faces representing front- and side-view faces are used in a template-matching process to detect faces within skin regions. The false-positive and false-negative errors of the proposed face-detection method on color images of size 300 × 220 and containing four or fewer faces are 0.04 or less.
