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Aiming at the occluded real‐world face images across illumination, pose, expression, and resolution variations, a robust face recognition for occluded real‐world images using constrained probabilistic sparse network is presented. A constrained probabilistic sparse representation network is constructed to obtain the features of all the training imag...
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p>Sparse representation-based face recognition has gained considerable attention recently due to its robustness against illumination and occlusion. Recognizing faces from videos has become a topic of importance to alleviate the limit of information content in still images. However, the sparse recognition framework is not applicable to video-based f...
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Aiming at the recognition of intelligent retail dynamic visual container goods, two problems that lead to low recognition accuracy must be addressed; one is the lack of goods features caused by the occlusion of the hand, and the other is the high similarity of goods. Therefore, this study proposes an approach for occluding goods recognition based on a generative adversarial network combined with prior inference to address the two abovementioned problems. With DarkNet53 as the backbone network, semantic segmentation is used to locate the occluded part in the feature extraction network, and simultaneously, the YOLOX decoupling head is used to obtain the detection frame. Subsequently, a generative adversarial network under prior inference is used to restore and expand the features of the occluded parts, and a multi-scale spatial attention and effective channel attention weighted attention mechanism module is proposed to select fine-grained features of goods. Finally, a metric learning method based on von Mises–Fisher distribution is proposed to increase the class spacing of features to achieve the effect of feature distinction, whilst the distinguished features are utilized to recognize goods at a fine-grained level. The experimental data used in this study were all obtained from the self-made smart retail container dataset, which contains a total of 12 types of goods used for recognition and includes four couples of similar goods. Experimental results reveal that the peak signal-to-noise ratio and structural similarity under improved prior inference are 0.7743 and 0.0183 higher than those of the other models, respectively. Compared with other optimal models, mAP improves the recognition accuracy by 1.2% and the recognition accuracy by 2.82%. This study solves two problems: one is the occlusion caused by hands, and the other is the high similarity of goods, thus meeting the requirements of commodity recognition accuracy in the field of intelligent retail and exhibiting good application prospects.
Despite the great progress that has been made in the task of single image dehazing, the results of the existing models in restoring image edge and texture information are still challenging. Besides, most dehazing models are trained on synthetic data, resulting in poor generalization ability to real‐world images. To address the aforementioned problems, a semi‐supervised learning dehazing method based on the decomposition model of Osher, Solé, and Vese(The OSV model) is presented. Specifically, the OSV model is first applied to decompose the hazy image into the structure layer and texture layer, save the texture layer and dehaze for the structure layer to restore images with sharper texture and edge. Furthermore, the network adopts a semi‐supervised learning algorithm based on generative adversarial networks (GAN) to generalize better to real‐world images, which includes two branches: supervised learning and unsupervised learning. Extensive experiments indicate that the proposed method preserves the texture and edge information of images more accurately while dehazing better, and performs favourably against the advanced dehazing algorithms on both synthetic outdoor datasets and real‐world hazy images.
In recent years, face recognition become a primordial technique in computer vision and machine learning. However, masks and many other accessories such as glasses, sunglasses and scarfs lead to face occlusion, which inhibits face recognition and degrades its performance. The automatic recognition of this kind of faces is challenging because: 1) the occluded area hides a significant part from the face, 2) there is a lack of annotated occluded face images for training, and 3) the various degraded conditions make the face recognition task more difficult and complex. To overcome these difficulties, we suggest a new approach for occluded face recognition grounded on the few-shot learning technique. Our suggested approach is based on a Siamese network based on the pre-trained Inception-v3 model for multi-class face recognition under degraded conditions. It aims to represent face images in a new embedding space by extracting the most significant features through the Inception-v3 network. Our proposed network is optimized utilizing the contrastive loss which is calculated between two input images. The suggested network overcomes the existing literature techniques and proves its performance against pre-trained models on both IST-EURECOM LFFD and EKFD datasets.
