Facial Expression Recognition via a Boosted Deep Belief Network

By Ping Liu, Shizhong Han, Zibo Meng, Yan Tong. 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014

610 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/c0c0b8558b17aa20debc4611275a4c69edd1e2a7

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In Experimental and Cognitive Psychology

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Citations in the corpus

Learning Social Relation Traits from Face Images (2015, background): “Social signal processing mainly involves facial expression recognition [23] and affective behaviour analysis [28].”
Deep Facial Expression Recognition: A Survey (2018, methodology): “[13] presented a boosted DBN (BDBN) that iteratively performs feature representation, feature selection and classifier construction in a unified loopy state.”
Facial Expression Analysis under Partial Occlusion (2018, background): “In recent years, deep learning approaches such as Boosted Deep Belief Network [Liu et al. 2014b], Convolutional Neural Networks (CNNs) [Chang et al. 2017], Long Short-Term Memory [Rodriguez et al. 2017], and fusion of multiple deep neural networks [Zhang et al. 2017], have gained increasing…”
Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network (2019, methodology): “o map human images into animated ones. Mollahosseini [19] proposed a neural network for FER using two convolution layers, one max pooling layer, and four “inception” layers, i.e. sub-networks. Liu in [20] combines the feature extraction and classiﬁcation in a single looped network, citing the two pa”
Automatic Analysis of Facial Actions: A Survey (2019, neutral): “, [80], [103].”
Persuasive Faces: Generating Faces in Advertisements (2018, background): “Facial expression and emotion recognition is an established and popular topic [4, 15, 23, 28, 32].”
Image based Static Facial Expression Recognition with Multiple Deep Network Learning (2015, background): “To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.”
Facial expression recognition with Convolutional Neural Networks: Coping with few data and the training sample order (2017, methodology): “Liu et al. [7] proposed a novel approach called Boosted Deep Belief Network (BDBN).”
Training deep networks for facial expression recognition with crowd-sourced label distribution (2016, background): “Lately, due to the great success of DCNN in a wide variety of image classification tasks [16, 28], it has also been applied in emotion recognition [14, 18, 17, 29, 33].”
From Facial Expression Recognition to Interpersonal Relation Prediction (2016, methodology): “[46] show the effectiveness of Boosted Deep Belief Network (BDBN) for end-to-end feature extraction and selection.”
Deep Learning in Spiking Neural Networks (2018, methodology): “ and then using Gibbs sampling to realize the appropriate weight update rules. DBNs and convolutional DBNs have successfully been employed in many areas such as visual processing [192], [193], [194], [195], audio processing [196], [197], [198], [199], [200], time series forecasting [201], and protei”
FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition (2016, background): “To further boost the performance, a loopy boosted deep belief network (DBN) framework was explored in [19].”
A comprehensive review on ensemble deep learning: Opportunities and challenges (2023, neutral): “In Liu et al. (2014) applied boosted deep belief network for facial expression recognition/shape changes based on the CK + database (contains 327 expression images) (Seyyedsalehi and Seyyedsalehi, 2014).”
Ensemble deep learning: A review (2021, methodology): “[59] Boosted deep belief network (DBN) as base classifiers for facial expression recognition.”
Facial Expression Recognition with Inconsistently Annotated Datasets (2018, background): “Based on these datasets, lots of FER approaches are proposed and achieve the state-of-the-art performance[27, 25, 30, 4, 14, 34, 18].”
Attention mechanism-based CNN for facial expression recognition (2020, methodology): “We compare our method with five existing methods [35][31][24][22] and the comparison results are provided in Table 7, which shows that the result of our method achieves 98.52% and outperforms the others on the JAFFE dataset.”