Real-time facial landmarks tracking using active shape model and LK optical flow

Ahn, Byung-Tae; Han, Yudeog; Kweon, In So

doi:10.1109/urai.2012.6463068

Cited by 14 publications

(9 citation statements)

References 4 publications

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“…These networks extract temporal features using geometric information of input face images. There are several geometric based approaches like Canny edge detection and AAM (active appearance model) [14], MRASM (multiresolution active shape model) and LK-flow [15] method. Facial landmark point is also one of the typical geometric feature.…”

Section: Temporal Geometric Networkmentioning

confidence: 99%

Real-time Facial Expression Recognition using 3D Appearance and Geometric Network for Public Security

Kim¹

2020

J. Def. Acquis. Technol.

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Facial expression recognition is a promising technology that can be used in various fields of artificial intelligence. There are several challenges to deal this task, one of which is recognizing a facial expression in sequential input data for real-time recognition. Many types of convolutional neural networks have been proposed to consider temporal information together. Typically, there are appearance networks that utilize facial appearance information, geometric networks that use facial landmark points, and hybrid approaches that combined these two networks. In this paper, we compare various methods proposed so far and identify the best approach for each network. We also have an experiment the initial architecture of our proposed 3D appearance and geometric network based on these researches and validate that its 97.22% accuracy on CK+ dataset is comparable to state-of-the-art techniques.

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Section: Temporal Geometric Networkmentioning

confidence: 99%

Real-time Facial Expression Recognition using 3D Appearance and Geometric Network for Public Security

Kim¹

2020

J. Def. Acquis. Technol.

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“…To improve real-time performance, a concept of ROI is proposed in [9] which means that detection is only executed within the region of interest. For instance, [10] use Calman filter to predict the next region of detection, [11] adopts Pyramid LK optical flow method and [12] adds Meanshift algorithm to TLD to get the region of interest. All the method above can accelerate the process of the tracker.…”

Section: B Existing Defects and Possible Solutionsmentioning

confidence: 99%

An Improved TLD Tracking Algorithm for Fast-moving Object

Zhou¹,

Peng²,

Gong³

et al. 2018

Proceedings of the 2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018)

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Traditional object tracking is easily affected by deformation, scale changes, illumination changes, partial occlusions and so on. TLD(Tracking-Learning-Detection) is a classic effective algorithm in long-term tracking which can solve these problems well. Meanwhile, the real-time performance of the system should be taken into account while in the actual situation. An improved fast-moving object tracking algorithm based on TLD is proposed in this paper. In the paper, a method of narrowing the region of detection is proposed to effectively minimize the consumption of time, the method is combined with self-prediction of motion direction to ensure the accuracy of detection. To compensate for the possible missing and false detections caused by the reduction of detection region and the changing background, the variance threshold is updated dynamically to let more possible correct bounding boxes pass the variance classifier. Experiments have been conducted to verify the improved TLD algorithm, the results show that our algorithm ensures the accuracy of object tracking and has a good performance on the real-time.

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“…The extended Active Shape Model(ASM) [4] is basically used with KLT feature tracker for accurate and stable extraction of eye appearance features [5]. 23 training samples were taken for gaze calibration as shown in Fig.…”

Section: Obtaining Eye Appearance Featurementioning

confidence: 99%

Appearance-based gaze estimation using kinect

Choi

Ahn

Parl

et al. 2013

2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

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Human gaze tracking has gathered much attention due to its capability to detect intuitive attention. Appearance-based methods can work with a single camera in ordinary conditions to track human gaze. An effective way to generate eye appearances is proposed using the Kinect. The head pose information is obtained from the Kinect after a series of calibrations. The Eye Appearance features are collected through ASM and KLT feature tracker. With 23 training samples, the error is found to be 1.07°. This paper proposes an efficient scheme for gaze tracking using a single Kinect device.

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Real-time facial landmarks tracking using active shape model and LK optical flow

Cited by 14 publications

References 4 publications

Real-time Facial Expression Recognition using 3D Appearance and Geometric Network for Public Security

Real-time Facial Expression Recognition using 3D Appearance and Geometric Network for Public Security

An Improved TLD Tracking Algorithm for Fast-moving Object

Appearance-based gaze estimation using kinect

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