Selective Transfer Machine for Personalized Facial Expression Analysis

Chu, Wen-Sheng; Torre, Fernando De la; Cohn, Jeffrey F.

doi:10.1109/tpami.2016.2547397

Cited by 179 publications

(109 citation statements)

References 79 publications

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“…This is a surprising result given that STM has outperformed linear SVM previously [6]. Two differences in the current work may account for this disparity.…”

Section: Baseline Results and Discussionmentioning

confidence: 57%

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Sayette Group Formation Task (GFT) Spontaneous Facial Expression Database

Girard

Chu

Jeni

et al. 2017

2017 12th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2017)

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Despite the important role that facial expressions play in interpersonal communication and our knowledge that interpersonal behavior is influenced by social context, no currently available facial expression database includes multiple interacting participants. The Sayette Group Formation Task (GFT) database addresses the need for well-annotated video of multiple participants during unscripted interactions. The database includes 172,800 video frames from 96 participants in 32 three-person groups. To aid in the development of automated facial expression analysis systems, GFT includes expert annotations of FACS occurrence and intensity, facial landmark tracking, and baseline results for linear SVM, deep learning, active patch learning, and personalized classification. Baseline performance is quantified and compared using identical partitioning and a variety of metrics (including means and confidence intervals). The highest performance scores were found for the deep learning and active patch learning methods. Learn more at http://osf.io/7wcyz.

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“…This is a surprising result given that STM has outperformed linear SVM previously [6]. Two differences in the current work may account for this disparity.…”

Section: Baseline Results and Discussionmentioning

confidence: 57%

“…Accounting for such differences has become a large area of research interest (e.g., [34]). To test the influence of such factors in the GFT database, we performed experiments using the Selective Transfer Machine (STM) approach [6]. The C and λ parameters were each tuned within {2 −5 , 2 −4 , …, 2 5 }.…”

Section: Baseline Methodsmentioning

confidence: 99%

Sayette Group Formation Task (GFT) Spontaneous Facial Expression Database

Girard

Chu

Jeni

et al. 2017

2017 12th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2017)

Self Cite

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“…The model named Selective Transfer Machine (STM) reweights the instance of personal faces to train a generic classifier. Most of instance-based transfer learning techniques use KMM to measure the difference of the distributions, and these methods are applied in many areas, such as facial action unit detection [25] and prostate cancer mapping [26].…”

Section: Domain Adaptationmentioning

confidence: 99%

Unsupervised Domain Adaptation Using Exemplar‐SVMs with Adaptation Regularization

Tian

Tang

et al. 2018

Complexity

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Domain adaptation has recently attracted attention for visual recognition. It assumes that source and target domain data are drawn from the same feature space but different margin distributions and its motivation is to utilize the source domain instances to assist in training a robust classifier for target domain tasks. Previous studies always focus on reducing the distribution mismatch across domains. However, in many real-world applications, there also exist problems of sample selection bias among instances in a domain; this would reduce the generalization performance of learners. To address this issue, we propose a novel model named Domain Adaptation Exemplar Support Vector Machines (DAESVMs) based on exemplar support vector machines (exemplar-SVMs). Our approach aims to address the problems of sample selection bias and domain adaptation simultaneously. Comparing with usual domain adaptation problems, we go a step further in slacking the assumption of i.i.d. First, we formulate the DAESVMs training classifiers with reducing Maximum Mean Discrepancy (MMD) among domains by mapping data into a latent space and preserving properties of original data, and then, we integrate classifiers to make a prediction for target domain instances. Our experiments were conducted on Office and Caltech10 datasets and verify the effectiveness of the model we proposed.

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“…While supervised learning has well-known advantages for event detection, limitations might be noted. One, because accuracy scales with increases in the number of subjects for whom annotated video is available, sufficient numbers of training subjects are essential [12, 25]. With too few training subjects, supervised learning is under-powered.…”

Section: Introductionmentioning

confidence: 99%

A Branch-and-Bound Framework for Unsupervised Common Event Discovery

et al. 2017

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Event discovery aims to discover a temporal segment of interest, such as human behavior, actions or activities. Most approaches to event discovery within or between time series use supervised learning. This becomes problematic when some relevant event labels are unknown, are difficult to detect, or not all possible combinations of events have been anticipated. To overcome these problems, this paper explores Common Event Discovery (CED), a new problem that aims to discover common events of variable-length segments in an unsupervised manner. A potential solution to CED is searching over all possible pairs of segments, which would incur a prohibitive quartic cost. In this paper, we propose an efficient branch-and-bound (B&B) framework that avoids exhaustive search while guaranteeing a globally optimal solution. To this end, we derive novel bounding functions for various commonality measures and provide extensions to multiple commonality discovery and accelerated search. The B&B framework takes as input any multidimensional signal that can be quantified into histograms. A generalization of the framework can be readily applied to discover events at the same or different times (synchrony and event commonality, respectively). We consider extensions to video search and supervised event detection. The effectiveness of the B&B framework is evaluated in motion capture of deliberate behavior and in video of spontaneous facial behavior in diverse interpersonal contexts: interviews, small groups of young adults, and parent-infant face-to-face interaction.

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Selective Transfer Machine for Personalized Facial Expression Analysis

Cited by 179 publications

References 79 publications

Sayette Group Formation Task (GFT) Spontaneous Facial Expression Database

Sayette Group Formation Task (GFT) Spontaneous Facial Expression Database

Unsupervised Domain Adaptation Using Exemplar‐SVMs with Adaptation Regularization

A Branch-and-Bound Framework for Unsupervised Common Event Discovery

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