Application of Face Recognition Method Under Deep Learning Algorithm in Embedded Systems

2021 ◽  
pp. 104034
Author(s):  
Xue Lv ◽  
Mingxia Su ◽  
Zekun Wang
Author(s):  
Wu Jianxing ◽  
Zeng Dexin ◽  
Ju Qiaodan ◽  
Chang Zixuan ◽  
Yu Hai

Background:: Owing to the ability of a deep learning algorithm to identify objects and the related detection technology of security inspection equipment, in this paper, we propose a progressive object recognition method that con-siders local information of objects. Methods:: First, we construct an X-Base model by cascading multiple convolutions and pooling layers to obtain the feature mapping image. Moreover, we provide a “segmented convolution, unified recognition” strategy to detect the size of the objects. Results:: Experimental results show that this method can effectively identify the specifications of bags passing through the security inspection equipment. Compared with the traditional VGG and progressive VGG recognition methods, the pro-posed method achieves advantages in terms of efficiency and concurrency. Conclusion:: This study provides a method to gradually recognize objects and can potentially assist the operators to identify prohibited objects.


Author(s):  
Sai Kiruthika K. M

The covid -19 is an unparalleled crisis resulting in huge number of casualties security problem. So has to scale back the spread of corona virus, people often wear a mask to guard themselves. Indeed, during this challenging context, the matter of face recognition is usually like periocular recognition involving iris, pupil, sclera, upper and lower eyelids, eye folds, eye corners, skin texture, fine wrinkles, complexion, skin color, skin pores etc. In this paper, we propose a reliable method supported discard masked region and deep learning based features so as to deal with the matter of masked face recognition process. The primary step to discard the masked face region. Next, we apply deep learning algorithm to extract the simplest features from obtained regions (mostly eyes and forehead regions). This leads to good accuracy than the previous work for detecting the masked face.


2017 ◽  
Vol 94 ◽  
pp. 115-124 ◽  
Author(s):  
Jianwei Zhao ◽  
Yongbiao Lv ◽  
Zhenghua Zhou ◽  
Feilong Cao

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