Face mask recognition system using MobileNetV2 with optimization function

Al-Rammahi, Atheer Hadi Issa

doi:10.1080/08839514.2022.2145638

Cited by 8 publications

(2 citation statements)

References 28 publications

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“…The difference in MobileNet V2 compared to the previous version is the addition of bottleneck and shortcut connection features [26], [27]. Currently, there are many studies using MobileNet V2, because this model gives better results with smaller parameters when using transfer learning from MobileNet V2 when compared to regular CNN [28]- [30]. In several studies also compared the VGG16 and MobileNet V2, and MobileNet V2 transfer learning methods had better accuracy [31].…”

Section: Related Existing Reviewmentioning

confidence: 99%

Face recognition for smart door security access with convolutional neural network method

Tribuana,

Hazriani,

Arda

2024

TELKOMNIKA

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This study focuses on enhancing office security through a smart door system, designed to protect sensitive documents and critical data. Emphasizing exclusive access for authorized personnel, the system integrates advanced biometric authentication, predominantly facial recognition. The project's aim is to optimize face recognition using convolutional neural network (CNN) techniques, identifying the best preprocessing methods and hyperparameter settings. A significant aspect of the research involves developing a smart door system with remote authentication and control capabilities via internet connectivity. Employing transfer learning with MobileNet V2, the study presents a compact model tailored for the Raspberry Pi platform. The model utilizes a dataset with five facial recognition classes and an additional class for unknown faces, ensuring a diverse representation. The trained model achieved a high accuracy (0.9729) and low loss (0.09). System evaluation revealed an overall accuracy of 0.96, perfect recall (1.00), and a precision of 0.897. These results demonstrate the system's efficacy in secure access control, making it a viable solution for contemporary office environments.

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Section: Related Existing Reviewmentioning

confidence: 99%

Face recognition for smart door security access with convolutional neural network method

Tribuana,

Hazriani,

Arda

2024

TELKOMNIKA

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“…Traditional face recognition technologies face many challenges in recognizing faces wearing masks, such as partial facial feature occlusion and light reflection caused by masks [6][7][8][9][10][11]. To address this issue, it is necessary to study a method that can effectively recognize and segment faces wearing masks [12][13][14][15][16][17][18][19][20]. By researching the face mask segmentation method that combines salient features and gender constraints, we can improve the face recognition accuracy under mask occlusion conditions, thus playing an important role in public safety, financial payment, access control systems, artificial intelligence monitoring, and health management.…”

Section: Introductionmentioning

confidence: 99%

Face Mask Segmentation Method Combining Salient Features and Gender Constraints

Xu¹,

Zheng²

2023

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With the global pandemic of COVID-19, masks have become essential items in public places, posing challenges to security and convenience facilities based on facial recognition technology, such as access control systems and payment systems. In existing solutions, gender constraints help improve the accuracy of face mask segmentation, but in some special cases, such as transgender people and individuals with ambiguous gender expressions, it may lead to gender misjudgment, affecting the segmentation results. Deep learning methods may increase computational complexity, impacting real-time performance. In scenarios where a large number of images need to be processed quickly, these methods may not meet real-time requirements. Therefore, this paper studies the face mask segmentation method combining salient features and gender constraints. To enable the model to perform real-time face detection on hardware platforms, we introduce depthwise separable convolution to optimize the multi-task cascaded convolutional neural network structure, accomplishing the face detection task that combines salient features and gender constraints. The extraction of the face mask region is completed, and the technical steps for face mask extraction based on spectral features are provided. Experimental results verify the effectiveness of the constructed model.

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