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ACM Symposium on Eye Tracking Research and Applications (ETRA)

作者： Larumbe, Andoni Cabeza, Rafael Villanueva, Arantxa Univ Publ Navarra Pamplona Spain

ISBN: (纸本)9781450357067

The precise detection of pupil/iris center is key to estimate gaze accurately. This fact becomes specially challenging in low cost frameworks in which the algorithms employed for high performance systems fail. In the last years an outstanding effort has been made in order to apply training-based methods to low resolution images. In this paper, supervised descent method (SDM) is applied to GI4E database. The 2D landmarks employed for training are the corners of the eyes and the pupil centers. In order to validate the algorithm proposed, a cross validation procedure is performed. The strategy employed for the training allows us to affirm that our method can potentially outperform the state of the art algorithms applied to the same dataset in terms of 2D accuracy. The promising results encourage to carry on in the study of training-based methods for eye tracking.

关键词： Eye tracking supervised descent method SDM Cascaded Regressors 2D iris center estimation

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supervised descent method Using Neural Networks for 2D Electrical Impedance Tomography

Supervised Descent Method Using Neural Networks for 2D Elect...

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International Applied-Computational-Electromagnetics-Society Symposium - China (ACES)

作者： Lin, Zhichao Guo, Rui Zhang, Ke Li, Maokun Yang, Fan Xu, Shenheng Abubakar, Aria Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Elect Engn State Key Lab Microwave & Digital Commun Beijing 100084 Peoples R China Schlumberger Houston TX USA

ISBN: (纸本)9780996007894

In this work, we study the application of the supervised descent method using neural networks (SDMNN) for 2D electrical impedance tomography. SDMNN contains the offline training and the online prediction stages. In the offline stage, neural networks are iteratively applied to learn a set of descent directions for minimizing the objective function;in the online stage, the trained neural networks can be directly used for the image reconstruction. This scheme combines the advantages of fast convergence speed of neural networks and good generalization ability of the supervised descent method (SDM). Numerical results verify the efficiency and accuracy of this method.

关键词： Electrical impedance tomography (EIT) generalization ability neural network supervised descent method

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Multi-task feature learning-based improved supervised descent method for facial landmark detection

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SIGNAL IMAGE AND VIDEO PROCESSING 2018年第1期12卷 17-24页

作者： Bian, Peng Xie, Zhengnan Jin, Yi North China Univ Technol Coll Mech & Mat Engn Beijing 100144 Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Beijing Key Lab Traff Data Anal & Min Beijing 100044 Peoples R China

Facial landmark detection has played an important role in many face understanding tasks, such as face verification, facial expression recognition, age estimation et al. Model initialization and feature extraction are crucial in supervised landmark detection. Mismatching caused by detector error and discrepant initialization is very common in these existing methods. To solve this problem, we have proposed a new method called multi-task feature learning-based improved supervised descent method (MtFL-iSDM) for the robust facial landmark localization. In this new method, firstly, a fast detection will be processed to locate the eyes and mouth, and the initialization model will adapt to the real location according to fast facial points detection. Secondly, multi-task feature learning is adopted on our improved supervised descent method model to achieve a better performance. Experiments on four benchmark databases show that our method achieves state-of-the-art performance.

关键词： Facial landmark detection Self-adapted model Feature selection supervised descent method Multi-task feature learning

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Random Subspace supervised descent method for Regression Problems in Computer Vision

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IEEE SIGNAL PROCESSING LETTERS 2015年第10期22卷 1816-1820页

作者： Yang, Heng Jia, Xuhui Patras, Ioannis Chan, Kwok-Ping Queen Mary Univ London London England Univ Hong Kong Hong Kong Peoples R China

supervised descent method (SDM) has shown good performance in solving non-linear least squares problems in computer vision, giving state of the art results for the problem of face alignment. However, when SDM learns the generic descent maps, it is very difficult to avoid over-fitting due to the high dimensionality of the input features. In this paper we propose a Random Subspace SDM (RSSDM) that maintains the high accuracy on the training data and improves the generalization accuracy. Instead of using all the features for descent learning at each iteration, we randomly select sub-sets of the features and learn an ensemble of descent maps in the corresponding subspaces, one in each subspace. Then, we average the ensemble of descents to calculate the update of the iteration. We test the proposed methods on two representative regression problems, namely, 3D pose estimation and face alignment and show that RSSDM consistently outperforms SDM in both tasks in terms of accuracy (e.g., RSSDM is able to localize 4% more landmarks at error level of 0.1 on the challenging iBug dataset). RSSDM also holds several useful generalization properties: 1) it is more effective when the number of training samples is small-with 3 Monte-Carlo permutations RSSDM can achieve similar performance to SDM with 9 Monte-Carlo permutations;2) it is less sensitive to the changes of the strength of the regularization-when the regularization parameter is changed to 10 times larger, the mean error increases 9.0% for SDM vs. 3.4% for RSSDM.

关键词： 3D pose estimation face alignment random subspace supervised descent method

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Investigating the use of the supervised descent method for Electromagnetic Imaging in PEC Enclosed Chambers 17

Investigating the use of the Supervised Descent Method for E...

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17th European Conference on Antennas and Propagation (EuCAP)

作者： Cathers, Seth Jeffrey, Ian Gilmore, Colin Univ Manitoba Elect & Comp Engn Winnipeg MB Canada

ISBN: (纸本)9788831299077

We consider the use of the supervised descent method (SDM) for 2D transverse magnetic imaging inside perfect electric conductor (PEC) enclosed electromagnetic imaging problems. When the electromagnetic imaging region is surrounded by PEC, and the background permittivity is lossless, classic optimization-based inversion algorithms such as Contrast Source Inversion sometimes provide degraded results compared to those obtained from unbounded domains. The SDM, by learning average search directions based on a large synthetic data set, may be able to avoid certain local minima that normally trap other optimization methods. Through the use of synthetic examples, we show that SDM can provide improved performance within an enclosed chamber.

关键词： Electromagnetic inversion PEC enclosures supervised descent method

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Block-regressed Face Alignment Algorithm Based on supervised descent method

Block-regressed Face Alignment Algorithm Based on Supervised...

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1st International Conference on Advanced Information Science and System (AISS)

作者： Shi, Yuqi Wuhan Univ Sci & Technol Int Coll Elect Informat Engn Wuhan Peoples R China

ISBN: (纸本)9781450372916

In recent years, the accuracy of face alignment has been improved a lot. However, the landmarks of each component in face are trained together in most algorithms, which ignore their dedicated characteristic and limit the further accuracy improvement. In this paper, we propose a new approach to improve the localization performance of facial landmarks by taking each component as independent task instead of the whole face. Namely, independent regressors are learned for each component using the gradient descent method. If only considering independent regressors for component, the inherent correlation between the components may be neglected. This paper proposed a strategy to effectively combine the results of the whole face regression and the independent components regressions. In this way, the effect of holistic and independent results are all taken into consideration, which can further enhance the alignment accuracy. A large number of experiments show that our method is better than the single loss function in both detection accuracy and reliability.

关键词： Face alignment supervised descent method

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Drowsiness Monitoring in Real-time based on supervised descent method 9

Drowsiness Monitoring in Real-time based on Supervised Desce...

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9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Neshov, Nikolay Manolova, Agata Tech Univ Sofia 8 Blvd Kliment Ohridski Sofia 1756 Bulgaria

ISBN: (纸本)9781538606971

With increased work load and unsuitable work shifts to survive in the fast paced world of today, people tend to lose sleep. Irregular sleep patterns and lack of sleep leads to drowsiness and fatigue. Drowsiness is perilous for the driver himself and for other drivers on the road and must be avoided for example by noise alerts in the car. This paper describes a method to detect drowsiness after implementing eye-tracking and mouth shape tracking in real-time. Viola-Jones algorithm is used to detect facial features in real-time. The proposed approach uses the detected facial features (i.e. eyes and mouth) based on supervised descent method to find the blinking rate of a driver as well as for yawning detection. A decision, whether the driver is vigilant or not is then provided. Real time experiments based on publicly available dataset prove that the proposed method is highly efficient in finding the drowsiness and alerting the driver.

关键词： drosiness detection yawning detection supervised descent method Viola-Jones facial featur blinking rate

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Facial Landmark Localization in Depth Images using supervised descent method 23

Facial Landmark Localization in Depth Images using Supervise...

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23nd Signal Processing and Communications Applications Conference (SIU)

作者： Camgoz, Necati Cihan Gokberk, Berk Akarun, Lale Bogazici Univ Bilgisayar Muhendisligi Bolumu Istanbul Turkey MEF Univ Bilgisayar Muhendisligi Bolumu Istanbul Turkey

ISBN: (纸本)9781467373869

This paper proposes using the state of the art 2D facial landmark localization method, supervised descent method (SDM), for facial landmark localization in 3D depth images. The proposed method was evaluated on frontal faces with no occlusion from the Bosphorus 3D Face Database. In the experiments, in which 2D features were used to train SDM, the proposed approach achieved state-of-the-art performance for several landmarks over the currently available 3D facial landmark localization methods.

关键词： supervised descent method 3D facial landmark localization face depth images

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3D Model-based Inversion with Limited Microwave Data Using supervised descent method 13

3D Model-based Inversion with Limited Microwave Data Using S...

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13th International Conference on Microwave and Millimeter Wave Technology (ICMMT)

作者： Jia, Zekui Guo, Rui Li, Maokun Yang, Fan Xu, Shenheng Wang, Guojun Liu, Zhiqu Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Elect Engn Beijing 100084 Peoples R China Lab Target Microwave Properties Deqing 313200 Zhejiang Peoples R China

ISBN: (纸本)9781728157337

In this work, we applied the supervised descent method (SDM) to 3D model-based inversion with limited microwave data. The target shapes are described by parameters based on prior information. The values of these parameters are reconstructed iteratively using SDM in which the descent directions are learned from data set constructed based on prior information. The model-based inversion has less non-uniqueness compared with typical pixel-based inversion. Furthermore, sufficient prior information allows SDM to predict the parameter values with a good accuracy, even with data of limited view. This algorithm is validated using synthetic data. The results show that we can correctly recover target shape parameters with only mono-static reflection data.

关键词： supervised descent method Model-based inversion Microwave data Limited view data Nonlinear inversion Prior information

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Facial Expression Classification Using supervised descent method Combined with PCA and SVM 1

Facial Expression Classification Using Supervised Descent Me...

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1st International Workshop on Biometrics (BIOMET)

作者： Manolova, Agata Neshov, Nikolay Panev, Stanislav Tonchev, Krasimir Tech Univ Sofia Fac Telecommun Sofia Bulgaria

ISBN: (纸本)9783319133867;9783319133850

It has been well known that there is a correlation between facial expression and person's internal emotional state. In this paper we use an approach to distinguish between neutral and some other expression: based on the displacement of important facial points (coordinates of edges of the mouth, eyes, eyebrows, etc.). Further the feature vectors are formed by concatenating the landmarks data from supervised descent method, applying PCA and use these data as an input to Support Vector Machine (SVM) classifier. The experimental results show improvement of the recognition rate in comparison to some state-of-the- art facial expression recognition techniques.

关键词： supervised descent method SVM PCA Facial expression Emotion recognition

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