May 7, 2019

3127 words 15 mins read

Paper Group ANR 18

From Facial Expression Recognition to Interpersonal Relation Prediction. Node-By-Node Greedy Deep Learning for Interpretable Features. SenTion: A framework for Sensing Facial Expressions. An end-to-end convolutional selective autoencoder approach to Soybean Cyst Nematode eggs detection. Cardiac Motion Analysis by Temporal Flow Graphs. Facial Expres …

From Facial Expression Recognition to Interpersonal Relation Prediction


Title	From Facial Expression Recognition to Interpersonal Relation Prediction
Authors	Zhanpeng Zhang, Ping Luo, Chen Change Loy, Xiaoou Tang
Abstract	Interpersonal relation defines the association, e.g., warm, friendliness, and dominance, between two or more people. Motivated by psychological studies, we investigate if such fine-grained and high-level relation traits can be characterized and quantified from face images in the wild. We address this challenging problem by first studying a deep network architecture for robust recognition of facial expressions. Unlike existing models that typically learn from facial expression labels alone, we devise an effective multitask network that is capable of learning from rich auxiliary attributes such as gender, age, and head pose, beyond just facial expression data. While conventional supervised training requires datasets with complete labels (e.g., all samples must be labeled with gender, age, and expression), we show that this requirement can be relaxed via a novel attribute propagation method. The approach further allows us to leverage the inherent correspondences between heterogeneous attribute sources despite the disparate distributions of different datasets. With the network we demonstrate state-of-the-art results on existing facial expression recognition benchmarks. To predict inter-personal relation, we use the expression recognition network as branches for a Siamese model. Extensive experiments show that our model is capable of mining mutual context of faces for accurate fine-grained interpersonal prediction.
Tasks	Facial Expression Recognition
Published	2016-09-21
URL	http://arxiv.org/abs/1609.06426v3
PDF	http://arxiv.org/pdf/1609.06426v3.pdf
PWC	https://paperswithcode.com/paper/from-facial-expression-recognition-to
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Node-By-Node Greedy Deep Learning for Interpretable Features


Title	Node-By-Node Greedy Deep Learning for Interpretable Features
Authors	Ke Wu, Malik Magdon-Ismail
Abstract	Multilayer networks have seen a resurgence under the umbrella of deep learning. Current deep learning algorithms train the layers of the network sequentially, improving algorithmic performance as well as providing some regularization. We present a new training algorithm for deep networks which trains \emph{each node in the network} sequentially. Our algorithm is orders of magnitude faster, creates more interpretable internal representations at the node level, while not sacrificing on the ultimate out-of-sample performance.
Tasks
Published	2016-02-19
URL	http://arxiv.org/abs/1602.06183v1
PDF	http://arxiv.org/pdf/1602.06183v1.pdf
PWC	https://paperswithcode.com/paper/node-by-node-greedy-deep-learning-for
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SenTion: A framework for Sensing Facial Expressions


Title	SenTion: A framework for Sensing Facial Expressions
Authors	Rahul Islam, Karan Ahuja, Sandip Karmakar, Ferdous Barbhuiya
Abstract	Facial expressions are an integral part of human cognition and communication, and can be applied in various real life applications. A vital precursor to accurate expression recognition is feature extraction. In this paper, we propose SenTion: A framework for sensing facial expressions. We propose a novel person independent and scale invariant method of extracting Inter Vector Angles (IVA) as geometric features, which proves to be robust and reliable across databases. SenTion employs a novel framework of combining geometric (IVA’s) and appearance based features (Histogram of Gradients) to create a hybrid model, that achieves state of the art recognition accuracy. We evaluate the performance of SenTion on two famous face expression data set, namely: CK+ and JAFFE; and subsequently evaluate the viability of facial expression systems by a user study. Extensive experiments showed that SenTion framework yielded dramatic improvements in facial expression recognition and could be employed in real-world applications with low resolution imaging and minimal computational resources in real-time, achieving 15-18 fps on a 2.4 GHz CPU with no GPU.
Tasks	Facial Expression Recognition
Published	2016-08-16
URL	http://arxiv.org/abs/1608.04489v1
PDF	http://arxiv.org/pdf/1608.04489v1.pdf
PWC	https://paperswithcode.com/paper/sention-a-framework-for-sensing-facial
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An end-to-end convolutional selective autoencoder approach to Soybean Cyst Nematode eggs detection


Title	An end-to-end convolutional selective autoencoder approach to Soybean Cyst Nematode eggs detection
Authors	Adedotun Akintayo, Nigel Lee, Vikas Chawla, Mark Mullaney, Christopher Marett, Asheesh Singh, Arti Singh, Greg Tylka, Baskar Ganapathysubramaniam, Soumik Sarkar
Abstract	This paper proposes a novel selective autoencoder approach within the framework of deep convolutional networks. The crux of the idea is to train a deep convolutional autoencoder to suppress undesired parts of an image frame while allowing the desired parts resulting in efficient object detection. The efficacy of the framework is demonstrated on a critical plant science problem. In the United States, approximately $1 billion is lost per annum due to a nematode infection on soybean plants. Currently, plant-pathologists rely on labor-intensive and time-consuming identification of Soybean Cyst Nematode (SCN) eggs in soil samples via manual microscopy. The proposed framework attempts to significantly expedite the process by using a series of manually labeled microscopic images for training followed by automated high-throughput egg detection. The problem is particularly difficult due to the presence of a large population of non-egg particles (disturbances) in the image frames that are very similar to SCN eggs in shape, pose and illumination. Therefore, the selective autoencoder is trained to learn unique features related to the invariant shapes and sizes of the SCN eggs without handcrafting. After that, a composite non-maximum suppression and differencing is applied at the post-processing stage.
Tasks	Object Detection
Published	2016-03-25
URL	http://arxiv.org/abs/1603.07834v1
PDF	http://arxiv.org/pdf/1603.07834v1.pdf
PWC	https://paperswithcode.com/paper/an-end-to-end-convolutional-selective
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Cardiac Motion Analysis by Temporal Flow Graphs


Title	Cardiac Motion Analysis by Temporal Flow Graphs
Authors	V S R Veeravasarapu, Jayanthi Sivaswamy, Vishanji Karani
Abstract	Cardiac motion analysis from B-mode ultrasound sequence is a key task in assessing the health of the heart. The paper proposes a new methodology for cardiac motion analysis based on the temporal behaviour of points of interest on the myocardium. We define a new signal called the Temporal Flow Graph (TFG) which depicts the movement of a point of interest over time. It is a graphical representation derived from a flow field and describes the temporal evolution of a point. We prove that TFG for an object undergoing periodic motion is also periodic. This principle can be utilized to derive both global and local information from a given sequence. We demonstrate this for detecting motion irregularities at the sequence, as well as regional levels on real and synthetic data. A coarse localisation of anatomical landmarks such as centres of left/right cavities and valve points is also demonstrated using TFGs.
Tasks
Published	2016-04-24
URL	http://arxiv.org/abs/1604.06979v1
PDF	http://arxiv.org/pdf/1604.06979v1.pdf
PWC	https://paperswithcode.com/paper/cardiac-motion-analysis-by-temporal-flow
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Facial Expression Recognition Using a Hybrid CNN-SIFT Aggregator


Title	Facial Expression Recognition Using a Hybrid CNN-SIFT Aggregator
Authors	Mundher Al-Shabi, Wooi Ping Cheah, Tee Connie
Abstract	Deriving an effective facial expression recognition component is important for a successful human-computer interaction system. Nonetheless, recognizing facial expression remains a challenging task. This paper describes a novel approach towards facial expression recognition task. The proposed method is motivated by the success of Convolutional Neural Networks (CNN) on the face recognition problem. Unlike other works, we focus on achieving good accuracy while requiring only a small sample data for training. Scale Invariant Feature Transform (SIFT) features are used to increase the performance on small data as SIFT does not require extensive training data to generate useful features. In this paper, both Dense SIFT and regular SIFT are studied and compared when merged with CNN features. Moreover, an aggregator of the models is developed. The proposed approach is tested on the FER-2013 and CK+ datasets. Results demonstrate the superiority of CNN with Dense SIFT over conventional CNN and CNN with SIFT. The accuracy even increased when all the models are aggregated which generates state-of-art results on FER-2013 and CK+ datasets, where it achieved 73.4% on FER-2013 and 99.1% on CK+.
Tasks	Face Recognition, Facial Expression Recognition
Published	2016-08-09
URL	http://arxiv.org/abs/1608.02833v5
PDF	http://arxiv.org/pdf/1608.02833v5.pdf
PWC	https://paperswithcode.com/paper/facial-expression-recognition-using-a-hybrid
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Beyond Exchangeability: The Chinese Voting Process


Title	Beyond Exchangeability: The Chinese Voting Process
Authors	Moontae Lee, Seok Hyun Jin, David Mimno
Abstract	Many online communities present user-contributed responses such as reviews of products and answers to questions. User-provided helpfulness votes can highlight the most useful responses, but voting is a social process that can gain momentum based on the popularity of responses and the polarity of existing votes. We propose the Chinese Voting Process (CVP) which models the evolution of helpfulness votes as a self-reinforcing process dependent on position and presentation biases. We evaluate this model on Amazon product reviews and more than 80 StackExchange forums, measuring the intrinsic quality of individual responses and behavioral coefficients of different communities.
Tasks
Published	2016-10-28
URL	http://arxiv.org/abs/1610.09428v1
PDF	http://arxiv.org/pdf/1610.09428v1.pdf
PWC	https://paperswithcode.com/paper/beyond-exchangeability-the-chinese-voting
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A Recursive Framework for Expression Recognition: From Web Images to Deep Models to Game Dataset


Title	A Recursive Framework for Expression Recognition: From Web Images to Deep Models to Game Dataset
Authors	Wei Li, Christina Tsangouri, Farnaz Abtahi, Zhigang Zhu
Abstract	In this paper, we propose a recursive framework to recognize facial expressions from images in real scenes. Unlike traditional approaches that typically focus on developing and refining algorithms for improving recognition performance on an existing dataset, we integrate three important components in a recursive manner: facial dataset generation, facial expression recognition model building, and interactive interfaces for testing and new data collection. To start with, we first create a candid-images-for-facial-expression (CIFE) dataset. We then apply a convolutional neural network (CNN) to CIFE and build a CNN model for web image expression classification. In order to increase the expression recognition accuracy, we also fine-tune the CNN model and thus obtain a better CNN facial expression recognition model. Based on the fine-tuned CNN model, we design a facial expression game engine and collect a new and more balanced dataset, GaMo. The images of this dataset are collected from the different expressions our game users make when playing the game. Finally, we evaluate the GaMo and CIFE datasets and show that our recursive framework can help build a better facial expression model for dealing with real scene facial expression tasks.
Tasks	Facial Expression Recognition
Published	2016-08-04
URL	http://arxiv.org/abs/1608.01647v1
PDF	http://arxiv.org/pdf/1608.01647v1.pdf
PWC	https://paperswithcode.com/paper/a-recursive-framework-for-expression
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Title	Multi-Modal Mean-Fields via Cardinality-Based Clamping
Authors	Pierre Baqué, François Fleuret, Pascal Fua
Abstract	Mean Field inference is central to statistical physics. It has attracted much interest in the Computer Vision community to efficiently solve problems expressible in terms of large Conditional Random Fields. However, since it models the posterior probability distribution as a product of marginal probabilities, it may fail to properly account for important dependencies between variables. We therefore replace the fully factorized distribution of Mean Field by a weighted mixture of such distributions, that similarly minimizes the KL-Divergence to the true posterior. By introducing two new ideas, namely, conditioning on groups of variables instead of single ones and using a parameter of the conditional random field potentials, that we identify to the temperature in the sense of statistical physics to select such groups, we can perform this minimization efficiently. Our extension of the clamping method proposed in previous works allows us to both produce a more descriptive approximation of the true posterior and, inspired by the diverse MAP paradigms, fit a mixture of Mean Field approximations. We demonstrate that this positively impacts real-world algorithms that initially relied on mean fields.
Tasks
Published	2016-11-23
URL	http://arxiv.org/abs/1611.07941v1
PDF	http://arxiv.org/pdf/1611.07941v1.pdf
PWC	https://paperswithcode.com/paper/multi-modal-mean-fields-via-cardinality-based
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A General Modifier-based Framework for Inconsistency-Tolerant Query Answering


Title	A General Modifier-based Framework for Inconsistency-Tolerant Query Answering
Authors	Jean Francois Baget, Salem Benferhat, Zied Bouraoui, Madalina Croitoru, Marie-Laure Mugnier, Odile Papini, Swan Rocher, Karim Tabia
Abstract	We propose a general framework for inconsistency-tolerant query answering within existential rule setting. This framework unifies the main semantics proposed by the state of art and introduces new ones based on cardinality and majority principles. It relies on two key notions: modifiers and inference strategies. An inconsistency-tolerant semantics is seen as a composite modifier plus an inference strategy. We compare the obtained semantics from a productivity point of view.
Tasks
Published	2016-02-18
URL	http://arxiv.org/abs/1602.05828v1
PDF	http://arxiv.org/pdf/1602.05828v1.pdf
PWC	https://paperswithcode.com/paper/a-general-modifier-based-framework-for
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Learning Schizophrenia Imaging Genetics Data Via Multiple Kernel Canonical Correlation Analysis


Title	Learning Schizophrenia Imaging Genetics Data Via Multiple Kernel Canonical Correlation Analysis
Authors	Owen Richfield, Md. Ashad Alam, Vince Calhoun, Yu-Ping Wang
Abstract	Kernel and Multiple Kernel Canonical Correlation Analysis (CCA) are employed to classify schizophrenic and healthy patients based on their SNPs, DNA Methylation and fMRI data. Kernel and Multiple Kernel CCA are popular methods for finding nonlinear correlations between high-dimensional datasets. Data was gathered from 183 patients, 79 with schizophrenia and 104 healthy controls. Kernel and Multiple Kernel CCA represent new avenues for studying schizophrenia, because, to our knowledge, these methods have not been used on these data before. Classification is performed via k-means clustering on the kernel matrix outputs of the Kernel and Multiple Kernel CCA algorithm. Accuracies of the Kernel and Multiple Kernel CCA classification are compared to that of the regularized linear CCA algorithm classification, and are found to be significantly more accurate. Both algorithms demonstrate maximal accuracies when the combination of DNA methylation and fMRI data are used, and experience lower accuracies when the SNP data are incorporated.
Tasks
Published	2016-09-15
URL	http://arxiv.org/abs/1609.04699v1
PDF	http://arxiv.org/pdf/1609.04699v1.pdf
PWC	https://paperswithcode.com/paper/learning-schizophrenia-imaging-genetics-data
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Peak-Piloted Deep Network for Facial Expression Recognition


Title	Peak-Piloted Deep Network for Facial Expression Recognition
Authors	Xiangyun Zhao, Xiaodan Liang, Luoqi Liu, Teng Li, Yugang Han, Nuno Vasconcelos, Shuicheng Yan
Abstract	Objective functions for training of deep networks for face-related recognition tasks, such as facial expression recognition (FER), usually consider each sample independently. In this work, we present a novel peak-piloted deep network (PPDN) that uses a sample with peak expression (easy sample) to supervise the intermediate feature responses for a sample of non-peak expression (hard sample) of the same type and from the same subject. The expression evolving process from non-peak expression to peak expression can thus be implicitly embedded in the network to achieve the invariance to expression intensities. A special purpose back-propagation procedure, peak gradient suppression (PGS), is proposed for network training. It drives the intermediate-layer feature responses of non-peak expression samples towards those of the corresponding peak expression samples, while avoiding the inverse. This avoids degrading the recognition capability for samples of peak expression due to interference from their non-peak expression counterparts. Extensive comparisons on two popular FER datasets, Oulu-CASIA and CK+, demonstrate the superiority of the PPDN over state-ofthe-art FER methods, as well as the advantages of both the network structure and the optimization strategy. Moreover, it is shown that PPDN is a general architecture, extensible to other tasks by proper definition of peak and non-peak samples. This is validated by experiments that show state-of-the-art performance on pose-invariant face recognition, using the Multi-PIE dataset.
Tasks	Face Recognition, Facial Expression Recognition, Robust Face Recognition
Published	2016-07-24
URL	http://arxiv.org/abs/1607.06997v2
PDF	http://arxiv.org/pdf/1607.06997v2.pdf
PWC	https://paperswithcode.com/paper/peak-piloted-deep-network-for-facial
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Confidence-Weighted Local Expression Predictions for Occlusion Handling in Expression Recognition and Action Unit detection


Title	Confidence-Weighted Local Expression Predictions for Occlusion Handling in Expression Recognition and Action Unit detection
Authors	Arnaud Dapogny, Kévin Bailly, Séverine Dubuisson
Abstract	Fully-Automatic Facial Expression Recognition (FER) from still images is a challenging task as it involves handling large interpersonal morphological differences, and as partial occlusions can occasionally happen. Furthermore, labelling expressions is a time-consuming process that is prone to subjectivity, thus the variability may not be fully covered by the training data. In this work, we propose to train Random Forests upon spatially defined local subspaces of the face. The output local predictions form a categorical expression-driven high-level representation that we call Local Expression Predictions (LEPs). LEPs can be combined to describe categorical facial expressions as well as Action Units (AUs). Furthermore, LEPs can be weighted by confidence scores provided by an autoencoder network. Such network is trained to locally capture the manifold of the non-occluded training data in a hierarchical way. Extensive experiments show that the proposed LEP representation yields high descriptive power for categorical expressions and AU occurrence prediction, and leads to interesting perspectives towards the design of occlusion-robust and confidence-aware FER systems.
Tasks	Action Unit Detection, Facial Expression Recognition
Published	2016-07-21
URL	http://arxiv.org/abs/1607.06290v1
PDF	http://arxiv.org/pdf/1607.06290v1.pdf
PWC	https://paperswithcode.com/paper/confidence-weighted-local-expression
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Dynamic Pose-Robust Facial Expression Recognition by Multi-View Pairwise Conditional Random Forests


Title	Dynamic Pose-Robust Facial Expression Recognition by Multi-View Pairwise Conditional Random Forests
Authors	Arnaud Dapogny, Kévin Bailly, Séverine Dubuisson
Abstract	Automatic facial expression classification (FER) from videos is a critical problem for the development of intelligent human-computer interaction systems. Still, it is a challenging problem that involves capturing high-dimensional spatio-temporal patterns describing the variation of one’s appearance over time. Such representation undergoes great variability of the facial morphology and environmental factors as well as head pose variations. In this paper, we use Conditional Random Forests to capture low-level expression transition patterns. More specifically, heterogeneous derivative features (e.g. feature point movements or texture variations) are evaluated upon pairs of images. When testing on a video frame, pairs are created between this current frame and previous ones and predictions for each previous frame are used to draw trees from Pairwise Conditional Random Forests (PCRF) whose pairwise outputs are averaged over time to produce robust estimates. Moreover, PCRF collections can also be conditioned on head pose estimation for multi-view dynamic FER. As such, our approach appears as a natural extension of Random Forests for learning spatio-temporal patterns, potentially from multiple viewpoints. Experiments on popular datasets show that our method leads to significant improvements over standard Random Forests as well as state-of-the-art approaches on several scenarios, including a novel multi-view video corpus generated from a publicly available database.
Tasks	Facial Expression Recognition, Head Pose Estimation, Pose Estimation
Published	2016-07-21
URL	http://arxiv.org/abs/1607.06250v1
PDF	http://arxiv.org/pdf/1607.06250v1.pdf
PWC	https://paperswithcode.com/paper/dynamic-pose-robust-facial-expression
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Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool


Title	Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool
Authors	Randal S. Olson, Jason H. Moore
Abstract	As data science continues to grow in popularity, there will be an increasing need to make data science tools more scalable, flexible, and accessible. In particular, automated machine learning (AutoML) systems seek to automate the process of designing and optimizing machine learning pipelines. In this chapter, we present a genetic programming-based AutoML system called TPOT that optimizes a series of feature preprocessors and machine learning models with the goal of maximizing classification accuracy on a supervised classification problem. Further, we analyze a large database of pipelines that were previously used to solve various supervised classification problems and identify 100 short series of machine learning operations that appear the most frequently, which we call the building blocks of machine learning pipelines. We harness these building blocks to initialize TPOT with promising solutions, and find that this sensible initialization method significantly improves TPOT’s performance on one benchmark at no cost of significantly degrading performance on the others. Thus, sensible initialization with machine learning pipeline building blocks shows promise for GP-based AutoML systems, and should be further refined in future work.
Tasks	AutoML
Published	2016-07-29
URL	http://arxiv.org/abs/1607.08878v1
PDF	http://arxiv.org/pdf/1607.08878v1.pdf
PWC	https://paperswithcode.com/paper/identifying-and-harnessing-the-building
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