Occluded Face Recognition Using Sparse Complex Matrix Factorization with Ridge Regularization

Putri, Diyah Utami Kusumaning; Musdholifah, Aina; Makhrus, Faizal; Duong, Viet Hang; Le, Phuong Thi; Chen, Bo-Wei; Wang, Jia-Ching

doi:10.1109/ispacs51563.2021.9651107

Cited by 1 publication

(1 citation statement)

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“…Faster-region-based convolutional neural network (Faster-RCNN) [ 10 ] and cascade R-CNN [ 11 ] are examples of multi-stage algorithms that divide feature extraction and regression tasks into two stages. Recognition of objects and faces under occlusion has advanced rapidly in recent years, thus yielding novel ideas and approaches for recognizing occluded commodities [ 12 , 13 , 14 ], which are broadly classified into two classes, one of which is adding weight to the unoccluded part and another is restoring the occluded part. Kortylewski et al [ 15 ] proposed a compositional convolutional neural network (CNN) model to recognize products based on unoccluded parts.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Occluded Goods under Prior Inference Based on Generative Adversarial Network

Cao

Xie

Liu

et al. 2023

Sensors

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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.

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