Random Forests for Real Time 3D Face Analysis

Fanelli, Gabriele; Dantone, Matthias; Gall, Jüergen; Fossati, Andrea; Gool, Luc Van

doi:10.1007/s11263-012-0549-0

Cited by 498 publications

(359 citation statements)

References 72 publications

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“…Biwi Kinect Head Pose dataset [25] was generated by computer vision laboratory of ETH Zurich in 2013 for estimating the location and orientation of a person's head from the depth data. This dataset is recorded when some people are facing to a Kinect (about one meter away) and turning their heads around randomly.…”

Section: Biwi Kinect Head Pose Datasetmentioning

confidence: 99%

RGB-D datasets using microsoft kinect or similar sensors: a survey

Cai

Han

Liu

et al. 2016

Multimed Tools Appl

130

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RGB-D data has turned out to be a very useful representation of an indoor scene for solving fundamental computer vision problems. It takes the advantages of the color image that provides appearance information of an object and also the depth image that is immune to the variations in color, illumination, rotation angle and scale. With the invention of the low-cost Microsoft Kinect sensor, which was initially used for gaming and later became a popular device for computer vision, high quality RGB-D data can be acquired easily. In recent years, more and more RGB-D image/video datasets dedicated to various applications have become available, which are of great importance to benchmark the stateof-the-art. In this paper, we systematically survey popular RGB-D datasets for different applications including object recognition, scene classification, hand gesture recognition, 3D-simultaneous localization and mapping, and pose estimation. We provide the insights into the characteristics of each important dataset, and compare the popularity and the difficulty of those datasets. Overall, the main goal of this survey is to give a comprehensive description about the available RGB-D datasets and thus to guide researchers in the selection of suitable datasets for evaluating their algorithms.

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Section: Biwi Kinect Head Pose Datasetmentioning

confidence: 99%

RGB-D datasets using microsoft kinect or similar sensors: a survey

Cai

Han

Liu

et al. 2016

Multimed Tools Appl

130

View full text Add to dashboard Cite

show abstract

“…Fanelli et al [22][23][24] adopted a voting method to directly determine head pose. However, their feature selection method for depth images degenerates into using 2D features, i.e., the RGB information used in 2D images was replaced by xyz-coordinate values in depth images.…”

Section: Head Pose Estimationmentioning

confidence: 99%

“…Papazov et al [25] also used a random forest-based framework, in a similar way to the methods in Refs. [22][23][24]. They replaced depth features by more elaborate triangular surface patch (TSP) features to ensure view-invariance.…”

Section: Head Pose Estimationmentioning

confidence: 99%

“…We mainly used three databases. Two were from the ETHZ-Computer Vision Lab [22][23][24] and available online, the BIWI Kinect Head Pose database (BIWI) and the BIWI 3D Audiovisual Corpus of Affective Communication database (B3D(AC) 2 ). In order to test our algorithm on different kinds of head pose and facial expression, we constructed our own 3D Faces with Expression and Pose database (3DFEP).…”

Section: Figurementioning

confidence: 99%

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Joint head pose and facial landmark regression from depth images

et al. 2017

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This paper presents a joint head pose and facial landmark regression method with input from depth images for realtime application. Our main contributions are: firstly, a joint optimization method to estimate head pose and facial landmarks, i.e., the pose regression result provides supervised initialization for cascaded facial landmark regression, while the regression result for the facial landmarks can also help to further refine the head pose at each stage. Secondly, we classify the head pose space into 9 sub-spaces, and then use a cascaded random forest with a global shape constraint for training facial landmarks in each specific space. This classification-guided method can effectively handle the problem of large pose changes and occlusion. Lastly, we have built a 3D face database containing 73 subjects, each with 14 expressions in various head poses. Experiments on challenging databases show our method achieves state-of-the-art performance on both head pose estimation and facial landmark regression.

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“…It can be described as four steps: (1) extract visual words from images by local feature descriptors like SURF [6], SIFT [7][8], PCA-SIFT [9], (2) construct the visual vocabulary by the clustering algorithms like k-means [10][11] and the random clustering forests [12], (3) quantify the images by the histogram of the extracted words in the vocabulary, and (4) use the sample images to train and test the classifier. The direct using of BOW model has been proved to be well applied to the classification of the repeated/near-duplicate images.…”

Section: Bag Of Words Modelmentioning

confidence: 99%

Objects Classification for Mobile Robots Using Hierarchic Selective Search Method

Cao

et al. 2016

MATEC Web Conf.

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Abstract. Aiming at determining the category of an image captured from mobile robots for intelligent application, classification with the bag-of-words model is proved effectively in near-duplicate/planar images. When it comes to images from mobile robots with complex background, does it still work well? In this paper, based on the merging criterion improvement, a method named hierarchical selective search is proposed hierarchically extracting complementary features to form a combined and environment-adaptable similarity measurement for segmentation resulting a small and high-quality regions set. Simultaneously those regions rather than a whole image are used for classification. As a result, it well improved the classification accuracy and make the bog-of-word model still work well on classification for mobile robots. The experiments on hierarchical selective search show its better performance than selective search on two task datasets for mobile robots. The experiments on classification shows the samples from regions are better than those original whole images. The advantage of less quantity and higher quality object regions from hierarchical selective search is more prominent when it comes to those special tasks for mobile robots with scarce data.

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Random Forests for Real Time 3D Face Analysis

Cited by 498 publications

References 72 publications

RGB-D datasets using microsoft kinect or similar sensors: a survey

RGB-D datasets using microsoft kinect or similar sensors: a survey

Joint head pose and facial landmark regression from depth images

Objects Classification for Mobile Robots Using Hierarchic Selective Search Method

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