Smart Attendance System based on improved Facial Recognition

Abstract

Nowadays, the fourth industrial revolution has achieved significant advancement in high technology, in which artificial intelligence has had vigorous development. In practice, facial recognition is one most essential tasks in the field of computer vision with various potential applications from security and attendance system to intelligent services. In this paper, we propose an efficient deep learning approach to facial recognition. The paper utilizes the architecture of improved FaceNet model based on MobileNetV2 backbone with SSD subsection. The improved architecture uses depth-wise separable convolution to reduce the model size and computational volume and achieve high accuracy and processing speed. To solve the problem of identifying a person entering and exiting an area and integrating on advanced mobile devices limits to (such as limited memory and on-device storage) highly mobile resources. Especially, our approach yields better results in practical application with more than 95% accuracy on a small dataset of the original face images. Obtained frame rate (25 FPS) is very favorable compared to the field of facial recognition using neural network. Besides, the deep learning based on solution could be applicable in many low-capacity hardware or optimize system’s resource. Finally, the smart automated attendance systems is successfully designed basing on the improved efficient facial recognition.

AI based Face Recognition Attendance System

“…The conclusion discusses the importance of staying current with technology trends in education and highlights the advantages of using facial recognition for attendance tracking, citing it as the most affordable and flexible option. Dang (2023) discusses the development of an efficient deep learning approach for facial recognition, particularly focusing on the architecture of an improved FaceNet model based on the MobileNetV2 backbone with SSD subsection. The proposed model [6] utilizes depth-wise separable convolution to reduce model size and computational volume while maintaining high accuracy and processing speed.…”

“…Dang (2023) discusses the development of an efficient deep learning approach for facial recognition, particularly focusing on the architecture of an improved FaceNet model based on the MobileNetV2 backbone with SSD subsection. The proposed model [6] utilizes depth-wise separable convolution to reduce model size and computational volume while maintaining high accuracy and processing speed.…”

An Ultra Fast Semantic Segmentation Model for AMR’s Path Planning

“…In Fig. 3, In addition to Depthwise Separable Convolutions, Linear bottlenecks and Inverted Residual Block (shortcut links between bottlenecks) are suggested for usage in MobileNetV2 [34]. Since the input and output of a block in a conventional residual architecture typically have more channels than the intermediary layers, MobileNet v2's residual block is the inverse of this design.…”

“…Therefore, the authors devise a segmentation model using FCN as the decoder and MobilenetV2 as the encoder in order to solve the semantic segmentation problem [34,35] and achieve efficient scene comprehension for autonomous driving. This combination will allow to perform the required tasks using only embedded computers while still achieving real-time performance quality.…”

Study on Tracking Real-Time Target Human Using Deep Learning for High Accuracy

“…This is also a good model, but the SSD model faces difficulty in predicting small object and needs a lot of data to train. In our recent paper [16], the combination of SSD-MobileNet-v2 [17,18] and particle filter is proposed. This algorithm is applied in the case of target humans having different colors and full occlusion problems.…”