BAUM-1: A Spontaneous Audio-Visual Face Database of Affective and Mental States

Zhalehpour, Sasan; Onder, Onur; Akhtar, Zahid; Erdem, Çiğdem Eroğlu

doi:10.1109/taffc.2016.2553038

Cited by 158 publications

(67 citation statements)

References 42 publications

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“…Finally, the global feature is fed into a classifier to recognize facial expression. Extensive experiments on two public databases, i.e., the BAUM-1 [9], and eNTERFACE05 [10], show the effectiveness of our method.…”

Section: Related Workmentioning

confidence: 84%

“…To evaluate the recognition accuracy of our framework, extensive experiments on two public datasets of emotional videos were conducted: BAUM-1 [9] and eNTERFACE05 [10]. The implementation details are as follows.…”

Section: Experiments Studies and Results Analysismentioning

confidence: 99%

“…Specifically, in comparison with the previous best work, our method separately improves accuracy from 45.04% to 46.51% for the BAUM-1s database and from 42.16% to 43.72% on the eNTERFACE05 database. The methods in references [9,50] employ hand-crafted methods and shallow learning neural networks to extract low-level features. This also demonstrates that our high-level features perform better than hand-crafted features and indicates the validity of the deep learning method.…”

Section: Comparisons With State-of-the-art Methodsmentioning

confidence: 99%

“…The BAUM-1s database [9] has 1222 video samples from 31 people. They exhibited joy, anger, sadness, disgust, fear, surprise, boredom, and contempt.…”

Section: Datasetsmentioning

confidence: 99%

“…Convolutional neural networks (CNNs) have improved image classification accuracy and outperform the hand-crafted feature method owing to the large-scale datasets [8]. However, current emotional datasets are small and have noise [9,10]. Furthermore, spatial signals and temporal signals are very important for modeling emotional video.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Deep Temporal–Spatial Aggregation for Video-Based Facial Expression Recognition

Pan

Guo

et al. 2019

Symmetry

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The proposed method has 30 streams, i.e., 15 spatial streams and 15 temporal streams. Each spatial stream corresponds to each temporal stream. Therefore, this work correlates with the symmetry concept. It is a difficult task to classify video-based facial expression owing to the gap between the visual descriptors and the emotions. In order to bridge the gap, a new video descriptor for facial expression recognition is presented to aggregate spatial and temporal convolutional features across the entire extent of a video. The designed framework integrates a state-of-the-art 30 stream and has a trainable spatial–temporal feature aggregation layer. This framework is end-to-end trainable for video-based facial expression recognition. Thus, this framework can effectively avoid overfitting to the limited emotional video datasets, and the trainable strategy can learn to better represent an entire video. The different schemas for pooling spatial–temporal features are investigated, and the spatial and temporal streams are best aggregated by utilizing the proposed method. The extensive experiments on two public databases, BAUM-1s and eNTERFACE05, show that this framework has promising performance and outperforms the state-of-the-art strategies.

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Section: Related Workmentioning

confidence: 84%

Section: Experiments Studies and Results Analysismentioning

confidence: 99%

Section: Comparisons With State-of-the-art Methodsmentioning

confidence: 99%

“…The BAUM-1s database [9] has 1222 video samples from 31 people. They exhibited joy, anger, sadness, disgust, fear, surprise, boredom, and contempt.…”

Section: Datasetsmentioning

confidence: 99%