Cross-database facial expression recognition based on hybrid improved unsupervised domain adaptation

Jin, Cong

doi:10.1007/s11042-022-13311-2

Cited by 4 publications

(4 citation statements)

References 57 publications

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“…The domain adaptive method is one of the feature-based transfer learning methods. The hybrid improved unsupervised cross-domain adaptation method proposed by Jin improved the separability of source domain samples [6]. It minimized the intra-class sample distance and maximized the inter-class sample distance.…”

Section: Domain Adaptive Methods For Facial Expression Recognitionmentioning

confidence: 99%

DA-FER: Domain Adaptive Facial Expression Recognition

Bie

Liu

et al. 2023

Applied Sciences

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Facial expression recognition (FER) is an important field in computer vision with many practical applications. However, one of the challenges in FER is dealing with small sample data, where the number of samples available for training machine learning algorithms is limited. To address this issue, a domain adaptive learning strategy is proposed in this paper. The approach uses a public dataset with sufficient samples as the source domain and a small sample dataset as the target domain. Furthermore, the maximum mean discrepancy with kernel mean embedding is utilized to reduce the disparity between the source and target domain data samples, thereby enhancing expression recognition accuracy. The proposed Domain Adaptive Facial Expression Recognition (DA-FER) method integrates the SSPP module and Slice module to fuse expression features of different dimensions. Moreover, this method retains the regions of interest of the five senses to accomplish more discriminative feature extraction and improve the transfer learning capability of the network. Experimental results indicate that the proposed method can effectively enhance the performance of expression recognition. Specifically, when the self-collected Selfie-Expression dataset is used as the target domain, and the public datasets RAF-DB and Fer2013 are used as the source domain, the performance of expression recognition is improved to varying degrees, which demonstrates the effectiveness of this domain adaptive method.

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Section: Domain Adaptive Methods For Facial Expression Recognitionmentioning

confidence: 99%

DA-FER: Domain Adaptive Facial Expression Recognition

Bie

Liu

et al. 2023

Applied Sciences

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“…Consequently, when the feature distribution of the target domain is different from that of the source domain, the performance of facial expression will suffer from degeneration. Therefore, several transfer learning or domain adaptation methods [24][25][26][27] have been proposed to exploit the knowledge learned from the source domain to promote the learning task in target domain. The most common transfer learning approach is to train a model in one domain and then fine-tune it in a related domain.…”

Section: Transfer Learning For Facial Expression Recognitionmentioning

confidence: 99%

“…Ji et al [24] proposed to adopt pseudo labels in the target domain and adversarial learning to align the source and target domains. Jin et al [26] developed an algorithm to match the data distribution and maximize the correlation of data between different domains, and maximize the separability on the source domain.…”

Section: Transfer Learning For Facial Expression Recognitionmentioning

confidence: 99%

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Transferable driver facial expression recognition based on joint discriminative correlation alignment network with enhanced feature attention

Chen,

Du,

Deng

et al. 2023

IET Intelligent Trans Sys

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Facial expression is closely related to the emotions of drivers, thus facilitating safe driving detection in advanced driving assistance system (ADAS). Recently, deep learning techniques have become prevalent for facial expression recognition. However, for driving scenarios, the facial expression recognition is mainly challenged by the problems of small sample size as well as effective feature representation. To address the above issues, this paper puts forward a transfer learning‐based method for driver facial expression recognition by fully exploiting the other sources of facial expression data that may obey different distributions. Specifically, an enhanced feature attention module is firstly devised such that affluent features with multi‐scales can be extracted and refined based on the spatial and channel attention mechanisms. Then, a joint correlation alignment loss is presented ensuring that the samples in the source and target domains are transformed into the shared common subspace to reduce difference of both the marginal and conditional distributions. Multiple transfer learning tasks on real‐world data are carried out to evaluate the proposed method. The experimental results show that our model achieves better recognition accuracy for driver facial expression compared with several traditional and deep learning‐based transfer learning algorithms.

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