Automatic Facial Expression Recognition

Whitehill, Jacob; Bartlett, Marian Stewart; Movellan, Javier R.

doi:10.1093/acprof:oso/9780195387643.003.0007

Cited by 24 publications

(9 citation statements)

References 58 publications

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“…are applied to either the whole-face or specific face regions to encode the texture. The superior performance of appearance based methods to the geometry based features is reported in [4]. The appearance-based methods generates high dimensional vector which are further represented in lower dimensional subspace by applying dimensionality reduction techniques, such as principal component analysis (PCA), linear discriminant analysis (LDA) etc.…”

Section: Introductionmentioning

confidence: 99%

Automatic facial expression recognition using features of salient facial patches

Happy

Routray

2015

IEEE Trans. Affective Comput.

492

236

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Extraction of discriminative features from salient facial patches plays a vital role in effective facial expression recognition. The accurate detection of facial landmarks improves the localization of the salient patches on face images. This paper proposes a novel framework for expression recognition by using appearance features of selected facial patches. A few prominent facial patches, depending on the position of facial landmarks, are extracted which are active during emotion elicitation. These active patches are further processed to obtain the salient patches which contain discriminative features for classification of each pair of expressions, thereby selecting different facial patches as salient for different pair of expression classes. One-against-one classification method is adopted using these features. In addition, an automated learning-free facial landmark detection technique has been proposed, which achieves similar performances as that of other state-of-art landmark detection methods, yet requires significantly less execution time. The proposed method is found to perform well consistently in different resolutions, hence, providing a solution for expression recognition in low resolution images. Experiments on CK+ and JAFFE facial expression databases show the effectiveness of the proposed system.

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Section: Introductionmentioning

confidence: 99%

Automatic facial expression recognition using features of salient facial patches

Happy

Routray

2015

IEEE Trans. Affective Comput.

492

236

View full text Add to dashboard Cite

show abstract

“…The geometric features are, however, very sensitive to the individual face shape configuration and are therefore less consistent in person independent scenarios. It is important to note that these two types of features have recently been shown to be complementary [52], hence hybrid systems similar to the one we propose are gaining popularity. An additional direction of research is to integrate temporal dimension into both appearance and geometric features when working with image sequences [21,60,19,48,32].…”

Section: Related Workmentioning

confidence: 88%

Fast facial expression recognition using local binary features and shallow neural networks

et al. 2018

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Facial expression recognition applications demand accurate and fast algorithms that can run in real-time on platforms with limited computational resources. We propose an algorithm that bridges the gap between precise but slow methods and fast but less precise methods. The algorithm combines gentle boost decision trees and neural networks. The gentle boost decision trees are trained to extract highly discriminative feature vectors (Local Binary Features) for each basic facial expression around distinct facial landmark points. These sparse binary features are concatenated and used to jointly optimize facial expression recognition through a shallow neural network architecture. The joint optimization improves the recognition rates of difficult expressions such as fear and sadness. Furthermore, extensive experiments in both within-and crossdatabase scenarios have been conducted on relevant benchmark data sets for facial expression recognition: CK+, MMI, JAFFE, and SFEW 2.0. The proposed method (LBF-NN) compares favorably with state-ofthe-art algorithms while achieving an order of magnitude improvement in execution time.

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“…Appearance features represent skin texture and its permutations and have been widely applied to facial expression analysis. Representative methods include SIFT [86], DAISY [86], Gabor jets [4], LBP [35], [84], Bag-of-Words model [60], [61], compositional [77] and others [72]. Dynamic features, a newly popular technique, encodes temporal information during the feature extraction stage.…”

Section: Related Workmentioning

confidence: 99%

Selective Transfer Machine for Personalized Facial Expression Analysis

Chu

Torre

Cohn

2017

IEEE Trans. Pattern Anal. Mach. Intell.

176

104

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Automatic facial action unit (AU) and expression detection from videos is a long-standing problem. The problem is challenging in part because classifiers must generalize to previously unknown subjects that differ markedly in behavior and facial morphology (e.g., heavy versus delicate brows, smooth versus deeply etched wrinkles) from those on which the classifiers are trained. While some progress has been achieved through improvements in choices of features and classifiers, the challenge occasioned by individual differences among people remains. Person-specific classifiers would be a possible solution but for a paucity of training data. Sufficient training data for person-specific classifiers typically is unavailable. This paper addresses the problem of how to personalize a generic classifier without additional labels from the test subject. We propose a transductive learning method, which we refer as a Selective Transfer Machine (STM), to personalize a generic classifier by attenuating person-specific mismatches. STM achieves this effect by simultaneously learning a classifier and re-weighting the training samples that are most relevant to the test subject. We compared STM to both generic classifiers and cross-domain learning methods on four benchmarks: CK+ [44], GEMEP-FERA [67], RU-FACS [4] and GFT [57]. STM outperformed generic classifiers in all.

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Automatic Facial Expression Recognition

Cited by 24 publications

References 58 publications

Automatic facial expression recognition using features of salient facial patches

Automatic facial expression recognition using features of salient facial patches

Fast facial expression recognition using local binary features and shallow neural networks

Selective Transfer Machine for Personalized Facial Expression Analysis

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