Abstract
Deep learning theory is widely used in face recognition. Combined with the needs of classroom attendance and students’ learning status monitoring, this article analyzes the YOLO (You Only Look Once) face recognition algorithms based on regression method. Aiming at the problem of small target missing detection in the YOLOv3 network structure, an improved YOLOv3 algorithm based on Bayesian optimization is proposed. The algorithm uses deep separable convolution instead of conventional convolution to improve the Darknet-53 basic network, and it reduces the amount of calculation and parameters of the network. A multiscale feature pyramid is built, and an attention guidance module is designed to strengthen multiscale fusion, detecting different sizes of targets. The loss function is improved to solve the imbalance of positive and negative sample distribution and the imbalance between simple samples and difficult samples. The Bayesian function is adopted to optimize the classifier and improve the classification efficiency and accuracy, ensuring the accuracy of small target detection. Five groups of comparative experiments are carried out on public COCO and VOC2012 datasets and self-built datasets. The experimental results show that the proposed improved YOLOv3 model can effectively improve the detection accuracy of multiple faces and small targets. Compared with the traditional YOLOv3 model, the mean mAP of the target is improved by more than 1.2%.
Highlights
In recent years, biometric authentication has been widely used in all walks of life. ere are mature technologies, such as fingerprint recognition, face recognition, and iris recognition [1], which have been applied in university classroom
YOLO is an end-to-end convolutional neural network for target detection. e YOLO grid cell has three prediction bounding boxes, which take the largest bounding box among intersection over union (IoU) of the current target box as the current target of prediction. e first two dimensions of the predicted output map are extracted feature dimensions. e third dimension is B ∗ (5 + C), where B is the number of bounding boxes predicted by each grid unit and 5 is one confidence level plus 4 coordinates (x, y, w, h). e bounding box with confidence less than threshold value set to 0 C is the number of bounding boxes
In order to solve the problem of low accuracy of the traditional object detection model, a new YOLOv3 model based on Bayesian optimization is proposed. e depth integral separation convolution is used to replace the standard convolution for information fusion, which reduces the amount of network structure parameters and calculation
Summary
Biometric authentication has been widely used in all walks of life. ere are mature technologies, such as fingerprint recognition, face recognition, and iris recognition [1], which have been applied in university classroom. In order to improve the effect of classroom learning, we need to use face recognition technology. E second is single-stage target detection algorithm, represented by single shot multi-box detector (SSD) [16,17,18,19] and YOLO [20, 21], which is based on regression and classification of target detection, learning from the Faster R-CNN, sacrificing a little speed to further improve the accuracy. In view of the misdetection rate of occluded targets in pedestrian detection by the YOLOv3 algorithm, the YOLOv3 network structure is improved [24], which can enhance the ability of multi-scale feature fusion. In view of the above problems, this article proposes an improved YOLOv3 network structure based on Bayesian optimization for face recognition and face state analysis in class. (4) e research team carried out experiments and result analysis in COCO, VOC, and self-built classroom face datasets, which further verified the face recognition efficiency of the network designed in this article and the effectiveness of the method
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