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Qingqing Wang

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EAAI Journal 2024 Journal Article

Adaptive graph-based feature normalization for facial expression recognition

Yu-Jie Xiong
Qingqing Wang
Yangtao Du
Yue Lu

Facial Expression Recognition (FER) suffers from data uncertainties caused by ambiguous facial images and annotators’ subjectiveness, resulting in excursive semantic and feature covariate shifting problems. Existing works usually correct mislabeled data by estimating noise distribution, or guide network training with knowledge domain that learned from clean data, neglecting the associative relations of expression samples. In this work, we propose an Adaptive Graph-based Feature Normalization (AGFN) to protect FER models from data uncertainties by normalizing feature distributions with the association of expressions. Specifically, we propose a Poisson graph generator to adaptively construct topological graphs for samples in each mini-batches via a sampling process, and correspondingly design a coordinate descent strategy to optimize proposed model. Our method outperforms state-of-the-art works with accuracies of 91. 84%, 91. 11% and 61. 38% on three benchmark datasets, i. e. , FERPlus, RAF-DB and AffectNet. Especially, when the percentage of mislabeled data significantly increases (e. g. , to 20%), our method surpasses existing works by 14. 09%, 21. 12% and 13. 67% on above datasets. Our code is publicly available in https: //github. com/X-Lab-CN/AGBFN.

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IJCAI Conference 2017 Conference Paper

A Sequence Labeling Convolutional Network and Its Application to Handwritten String Recognition

Qingqing Wang
Yue Lu

Handwritten string recognition has been struggling with connected patterns fiercely. Segmentation-free and over-segmentation frameworks are commonly applied to deal with this issue. For the past years, RNN combining with CTC has occupied the domain of segmentation-free handwritten string recognition, while CNN is just employed as a single character recognizer in the over-segmentation framework. The main challenges for CNN to directly recognize handwritten strings are the appropriate processing of arbitrary input string length, which implies arbitrary input image size, and reasonable design of the output layer. In this paper, we propose a sequence labeling convolutional network for the recognition of handwritten strings, in particular, the connected patterns. We properly design the structure of the network to predict how many characters present in the input images and what exactly they are at every position. Spatial pyramid pooling (SPP) is utilized with a new implementation to handle arbitrary string length. Moreover, we propose a more flexible pooling strategy called FSPP to adapt the network to the straightforward recognition of long strings better. Experiments conducted on handwritten digital strings from two benchmark datasets and our own cell-phone number dataset demonstrate the superiority of the proposed network.

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