Voiceprint recognition check-in system based on deep learning

With the development of modern technology and the rise of artificial intelligence, the application scenarios of identity authentication technology are becoming more and more complex, especially in the current situation of the spread of the new coronavirus, traditional identity authentication technology can no longer meet people's practical needs, and society urgently needs a security and convenient authentication technology. Voiceprint recognition is a kind of biometric technology, and it is one of the products of comprehensive research on computer technology, acoustics and life sciences. This paper introduces a voiceprint recognition check-in system based on deep learning algorithm. In this design, the audio is converted into Mel frequency cepstral coefficients, and then the convolution network is provided to extract features. Finally, the similarity is calculated to obtain the classification result for voiceprint feature extraction, which is compared with the voice database data to realize voiceprint recognition. The voiceprint recognition check-in system introduced in this paper has a check-in system with an interactive interface. The average recognition rate of the system measured by experiments is higher than 93.3%, which can meet the requirements of practical applications. © 2022 IEEE.

Triplet Decoupling Network for Masked Face Verification

Face verification has been widely applied to identity authentication in many areas. However, due to the mask information embedded into the facial feature representation, existing face verification systems generally fail to identify the faces covered by masks during the COVID-19 coronavirus epidemic period. To address this issue, we propose a new triplet decoupling network (TDN) for the challenging masked face verification. Different from existing works, our proposed TDN seeks to remove the mask information included in extracted facial features by feature decoupling, such that more discriminative facial feature representations can be obtained for masked face verification. In addition, a new triplet similarity margin loss (TSM) is designed to enlarge the margin between the intra-class similarity and the inter-class similarity of faces. Experimental results show that the proposed method significantly outperforms the other state-of-the-art methods on masked face datasets, which demonstrates the effectiveness of our proposed method. © 2021 IEEE.

RFID Based Identity Authentication System in COVID-19 Era

To control the COVID-19 outbreak, the Malaysia government has to tighten the rules and add on some standard operating procedures (SOP) for all premises. There will be an entrance registration for people that enter any shops, malls, schools, or offices. This entrance registration will take their identities, such as name, contact number, and current temperature. Thus, the government can easily track down and notify the person if the virus transmission occurs. This paper is mainly about improving the daily registration system to monitor the movement of Malaysians during the Covid-19 outbreak. With that needs in mind, a Radio-frequency Identification (RFID) based identity authentication system is developed and presented in this paper. Users do not need to fill in the manual form or scan the Quick Response (QR) code repeatedly, and instead, they are required to just key in the personal data once at the entrance. The RFID tag is applicable to be used as a self-registration at all premises. It can also keep track of the user identity, and the data will be recorded automatically through a monitoring application every time the users enter or leave the premises. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Lightweight Face Verification Based on Adaptive Cascade Network and Triplet Loss Function

In the past few years, with the continuous breakthrough of technology in various fields, artificial intelligence has been considered as a revolutionary technology. One of the most important and useful applications of artificial intelligence is face detection. The outbreak of COVID-19 has promoted the development of the noncontact identity authentication system. Face detection is also one of the key techniques in this kind of authentication system. However, the current real-time face detection is computationally expensive which hinders the application of face recognition. To address this issue, we propose a face verification framework based on adaptive cascade network and triplet loss. The framework is simple in network architecture and has light-weighted parameters. The training network is made of three stages with an adaptive cascade network and utilizes a novel image pyramid based on scales with different sizes. We train the face verification model and complete the verification within 0.15 second for processing one image which shows the computation efficiency of our proposed framework. In addition, the experimental results also show the competitive accuracy of our proposed framework which is around 98.6%. Using dynamic semihard triplet strategy for training, our network achieves a classification accuracy of 99.2% on the dataset of Labeled Faces in the Wild. © 2022 Jianhong Lin et al.