Noisy labels in Facial Expression Recognition (FER) datasets severely affect the performance of FER models. We propose a novel dual-branch noise extraction and suppression method to address this issue. This algorithm reduces the model’s impact from noisy labels by decreasing the dataset noise ratio and suppressing label-noisy samples. The method comprises three primary stages: sample extraction, pseudo-label generation, and re-training. The approach initially extracts label-noisy samples from the dataset by computing an exponential moving average of the model predictions and the joint probability distribution matrix of noisy and actual labels. The remaining samples form a clean dataset. Next, the training weights of the clean dataset are utilized to assign appropriate pseudo-labels to the label-noisy samples. Subsequently, the noisy labels are replaced with pseudo-labels to create a corrected dataset. The corrected and clean datasets are combined to create the reconstructed dataset, reducing noisy labels within the dataset. Finally, the model is retrained using the reconstructed dataset. Furthermore, this study introduces a novel gradient suppression smoothing function specifically designed to mitigate the impact of label-noisy samples in the dataset during the re-training process. The proposed algorithm is robust, with accuracies of 91.17%, 91.56%, and 91.58% on the RAF-DB dataset with 10%, 20%, and 30% noisy labels, and accuracies of 89.91%, 90.17%, and 89.69% on the corresponding FERPlus.

As personal photos are widely shared on social media, face anonymization becomes an effective solution to avoid identity leakage. Aiming at the problems of low quality and uncontrollable attributes of anonymized faces in existing algorithms, we propose a face anonymization algorithm PICLAnony based on parametric imitation contrast learning. It transfers the four visual information of identity, expression, pose and illumination from the source image to the generated anonymized face image by parametric imitation contrast learning. And it edits these attribute features that reflect sensitive behavioral intentions under the premise of controllable background. In the parameter imitation learning stage, high-quality and pose-controllable anonymized faces are generated by imitating the semantic parameters of source images. In the parameter contrast learning stage, the semantic parameters of the edited generated image and the source image are compared and learned, which solves the problem of insufficient decoupling of expression and illumination attributes. In addition, a background control module is designed to keep the background controllable during the editing process of anonymous face facial attributes. The subjective and objective results demonstrate that PICLAnony outperforms the state-of-the-art methods in terms of image quality and editing of facial attributes of anonymized faces.

The eye area, which can effectively convey social information and behavioral intentions, contains most of the information in facial images. However, the occlusion of glasses makes it difficult to accurately obtain the eye information in some facial images. Although the existing attribute editing method can remove glasses from face images, there are existing drawbacks lay in incomplete eye information and insufficient disentanglement after the glasses were removed (e.g. the background and hair color also changed when the glasses were removed), unable to get real and natural eye editing results. To address this problem, we propose an eye attribute editing model assisted by dual-coordinate system and multi-probability fusion prediction. The former one is designed to enhance the position awareness of inpainting to directly focus on the eyes area while the later one could integrate both local and global information of the target area. Specifically, the first stage of our method is to remove the glasses, and then use multi-probability fusion prediction method to inpaint the eye area to obtain a reasonably complete eye editing result supported by dual-coordinate system. On the one hand, we designed a dual-coordinate positioning module that uses a cascaded classifier and a dual-coordinate system to locate the human eye height area mask, which effectively solves the problem of inconsistent eye position height in facial images and determines the editing area for inpainting after the glasses are removed. On the other hand, we employed a multi-probability fusion prediction algorithm to supplement and enhance the ocular information, which can combine a distance-based local probability and an adaptive global probability, resulting in improved editing results. Quantitative and qualitative evaluations show that our method outperforms the state-of-the-art methods.

Abstract Improving the accuracy and disentanglement of attribute translation, and maintaining the consistency of face identity have been hot topics in face attribute translation. Recent approaches employ attention mechanisms to enable attribute translation in facial images. However, due to the lack of accuracy in the extraction of style code, the attention mechanism alone is not precise enough for the translation of attributes. To tackle this, we introduce a style translator module, which partitions the style code into attribute‐related and unrelated components, enhancing latent space disentanglement for more accurate attribute manipulation. Additionally, many current methods use per‐pixel loss functions to preserve face identity. However, this can sacrifice crucial high‐level features and textures in the target image. To address this limitation, we propose a multiple‐perceptual reconstruction loss to better maintain image fidelity. Extensive qualitative and quantitative experiments in this article demonstrate significant improvements over state‐of‐the‐art methods, validating the effectiveness of our approach.

Fabric defect detection methods based on deep networks are widely used in the textile industry , but they often suffer from poor model generalization and blurry edge detection. To resolve these challenges, we propose a novel network called “U-SMR Net”, which integrates global contextual features, defect detail features, and high-level semantic features through the combination of ResNet-50 and Swin Transformer modules. Our U-SMR network includes a lightweight multiscale feature extraction module, the dual-branch pyramid Module (DBPM), which is nested to preserve high-resolution, shallow semantic information. We propose a recursive multi-level residual decoding block for multiscale fusion to refine, filter, and enhance input characteristics, generating prediction maps at multiple stages, and by employing an improved binary cross entropy loss function to supervise saliency mapping. The experimental results based on four groups from ZJU-Leaper dataset demonstrate the superior performance of our approach compared to other competitive methods by achieving an average f m e a s u r e score of 75.33%, and finally testing results from both ZJU-Leaper-Total dataset and the HKU-Fabric dataset further support our U-SMR Net’s validity and generalization ability.

Abstract The absence of local features and global shape constraints severely limits the performance of the hourglass network for facial landmark detection in unconstrained environments. Moreover, diverse feature types and scales may result in low accuracy. This paper proposes a probability‐guided hourglass network to enhance the shape constraints for robust facial landmark detection. Firstly, a multi‐scale pre‐processing module is designed to extract features at different scales. Secondly, based on the heatmaps generated by the stacked hourglass network, the coarse localizations are obtained, while the probability maps are generated with local features. Finally, a probability‐based boundary regression method is proposed and the hausdorff distance is modified as the loss function to constrain the feature shape. Adaptive weights are also added to the loss function, which can help relieve the data imbalance problem. Subjective and objective experimental results on the challenging datasets show that this method outperforms the state‐of‐the‐arts on unconstrained conditions.

In this study, the authors investigate the problem of enhanced image no‐reference (NR) quality assessment. For resolving the problem of the enhanced images, it is difficult to obtain reference images, this study proposes an NR image quality assessment (IQA) model based on colour space distribution. Given an enhanced image, our method first uses a gist to select a clear target image in which the scene, colour and quality are similar to the hypothetical reference images. And then, the colour transfer is used between the input images and target images to construct the reference image. Next, the appropriate IQA method is used to assess enhanced image quality. The absolute colour difference and feature similarity (FSIM) are used to measure the colour and grey‐scale image quality, respectively. Extensive experiments demonstrate that the proposed method is good at evaluating enhanced image quality for X‐ray, dust, underwater and low‐light images. The experimental results are consistent with human subjective evaluation and achieve good assessment effects.