RFR-DLVT: a hybrid method for real-time face recognition using deep learning and visual tracking

Lei, Zhenfeng; Zhang, Xiaoying; Yang, Shuangyuan; Ren, Zihan; Akindipe, Olusegun F.

doi:10.1080/17517575.2019.1668964

Cited by 14 publications

(9 citation statements)

References 16 publications

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“…An image tracking system generally refers to a system used to track the travel path of an object recorded by CCTV (closed-circuit television) in a connected video system [ 20 , 21 ]. The primary purpose of image tracking systems is to track the travel path of a specific individual; they are used for purposes that require the need to continuously monitor or detect the behavior of a specific individual in real time or in the future [ 22 , 23 ]. The video surveillance system in China can serve as an example.…”

Section: Related Studiesmentioning

confidence: 99%

De-Identification Mechanism of User Data in Video Systems According to Risk Level for Preventing Leakage of Personal Healthcare Information

Kim

Park

2022

Sensors

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A problem with biometric information is that it is more sensitive to external leakage, because it is information that cannot be changed immediately compared to general authentication methods. Regarding facial information, a case in which authentication was permitted by facial information output by a 3D printer was found. Therefore, a method for minimizing the leakage of biometric information to the outside is required. In this paper, different levels of identification information according to the authority of the user are provided by the de-identification of metadata and face information in stages. For face information and metadata, the level of de-identification is determined and achieved according to the risk level of the de-identified subject. Then, we propose a mechanism to minimize the leakage path by preventing reckless data access by classifying access rights to unidentified data according to four roles. The proposed mechanism provides only differentially de-identified data according to the authority of the accessor, and the required time to perform the de-identification of one image was, on average, 3.6 ms for 300 datapoints, 3.5 ms for 500 datapoints, and 3.47 ms for 1000 datapoints. This confirmed that the required execution time was shortened in proportion to the increase in the size of the dataset. The results for the metadata were similar, and it was confirmed that it took 4.3 ms for 300 cases, 3.78 ms for 500 cases, and 3.5 ms for 1000 cases.

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Section: Related Studiesmentioning

confidence: 99%

De-Identification Mechanism of User Data in Video Systems According to Risk Level for Preventing Leakage of Personal Healthcare Information

Kim

Park

2022

Sensors

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“…In Ref. [5], the proposed system is using a deep learning and visual tracking (DLVT) for real-time face recognition. Input image of face is given to the system, the values of all the facial features are calculated and then it will be examined that the input face exists or not.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning for Masked Face Recognition in COVID-19 Pandemic Situation

Fatima¹,

Ghauri²,

Mohammad³

et al. 2022

MMEP

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In the current epidemic, the whole world is suffering with the infectious disease i.e., Corona Virus Disease (COVID-19). It is important to wear a mask to minimize the transmission of the disease. When everyone is wearing a face mask, it is difficult for recognition systems to recognize the masked face of a specific person. As some of the facial features are covered behind the mask e.g., mouth and nose. Therefore, the face-recognizing systems are inefficient to recognize the masked faces. To solve this issue, a face recognition system is proposed to recognize masked and unmasked faces. Support vector machine (SVM) and Random Forest (RF) based classifiers are trained on the specific dataset and classifiers effectively recognize the masked and unmasked faces. The classifier recognizes the human facial features such as eyes, eyebrows, forehead, ears, and hair. The dataset is collected in the form of images for 28 classes with and without a face mask. The trained system will recognize the person, whether the person is wearing a mask or not. The recognition accuracy is approximately 98.2% for different classes and the proposed recognizer is also compared with the state of art existing techniques.

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“…Recently, face recognition (FR) in videos has become a core research issue in the field of computer vision. In this paper (Lei et al 2019) has proposed a hybrid method based on Deep Learning (DL) and visual tracking, RFR-DLVT, to achieve effective face recognition (FR). First, video sequences are divided into reference frames (RFs) and non-reference frames (NRFs).…”

Section: Cognitive Analytics For Eismentioning

confidence: 99%

Cognitive computing for big data systems over internet of things for enterprise information systems

Sangaiah

Chaudhary

Tsai

et al. 2020

Enterprise Information Systems

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RFR-DLVT: a hybrid method for real-time face recognition using deep learning and visual tracking

Cited by 14 publications

References 16 publications

De-Identification Mechanism of User Data in Video Systems According to Risk Level for Preventing Leakage of Personal Healthcare Information

De-Identification Mechanism of User Data in Video Systems According to Risk Level for Preventing Leakage of Personal Healthcare Information

Machine Learning for Masked Face Recognition in COVID-19 Pandemic Situation

Cognitive computing for big data systems over internet of things for enterprise information systems

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