SqueezeFace: Integrative Face Recognition Methods with LiDAR Sensors

Ko, Kyoungmin; Gwak, Hyunmin; Thoummala, Nalinh; Kwon, Hyun; Kim, Sunghwan

doi:10.1155/2021/4312245

Cited by 14 publications

(9 citation statements)

References 30 publications

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“…It has been used as an observation technology for precise atmospheric analysis and global environmental observation via mounting on aircraft and satellites, and as a important technology for laser scanners and 3D imaging cameras in autonomous driving. Recently, mobile applications that use LiDAR for facial recognition and clothes measurement had also been studied [35], [36]. The sensor generates point-cloud data, which is a 3D representation of the target.…”

Section: Lidar Sensor and Point Cloudmentioning

confidence: 99%

CloudNet: A LiDAR-Based Face Anti-Spoofing Model That Is Robust Against Light Variation

Kim

Gwak

et al. 2023

IEEE Access

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Face anti-spoofing (FAS) is a technology that protects face recognition systems from presentation attacks. The current challenge faced by FAS studies is the difficulty in creating a generalized light variation model. This is because face data are sensitive to light domain. FAS models using only red green blue (RGB) images suffer from poor performance when the training and test datasets have different light variations. To overcome this problem, this study focuses on light detection and ranging (LiDAR) sensors. LiDAR is a time-of-flight depth sensor that is included in the latest mobile devices. It is negligibly affected by light and provides 3D coordinate and depth information of the target. Thus, a model that is resistant to light variations and exhibiting excellent performance can be created. For the experiment, datasets collected with a LiDAR camera are built and CloudNet architectures for RGB, point clouds, and depth are designed. Three protocols are used to confirm the performance of the model according to variations in the light domain. Experimental results indicate that for protocols 2 and 3, CloudNet error rates increase by 0.1340 and 0.1528, whereas the error rates of the RGB model increase by 0.3951 and 0.4111, respectively, as compared with protocol 1. These results demonstrate that the LiDAR-based FAS model with CloudNet has a more generalized performance compared with the RGB model.

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Section: Lidar Sensor and Point Cloudmentioning

confidence: 99%

CloudNet: A LiDAR-Based Face Anti-Spoofing Model That Is Robust Against Light Variation

Kim

Gwak

et al. 2023

IEEE Access

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“…The core technology responsible for this process is called TrueDepth. The system uses LEDs to project an irregular grid of over 30,000 infrared dots to record depth within a matter of milliseconds [ 20 , 21 ] and can provide a rapid, reliable, and direct method for producing 3D data [ 1 , 22 , 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

Smartphone-Based Facial Scanning as a Viable Tool for Facially Driven Orthodontics?

Thurzo

Strunga

Havlínová

et al. 2022

Sensors

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The current paradigm shift in orthodontic treatment planning is based on facially driven diagnostics. This requires an affordable, convenient, and non-invasive solution for face scanning. Therefore, utilization of smartphones’ TrueDepth sensors is very tempting. TrueDepth refers to front-facing cameras with a dot projector in Apple devices that provide real-time depth data in addition to visual information. There are several applications that tout themselves as accurate solutions for 3D scanning of the face in dentistry. Their clinical accuracy has been uncertain. This study focuses on evaluating the accuracy of the Bellus3D Dental Pro app, which uses Apple’s TrueDepth sensor. The app reconstructs a virtual, high-resolution version of the face, which is available for download as a 3D object. In this paper, sixty TrueDepth scans of the face were compared to sixty corresponding facial surfaces segmented from CBCT. Difference maps were created for each pair and evaluated in specific facial regions. The results confirmed statistically significant differences in some facial regions with amplitudes greater than 3 mm, suggesting that current technology has limited applicability for clinical use. The clinical utilization of facial scanning for orthodontic evaluation, which does not require accuracy in the lip region below 3 mm, can be considered.

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“…The core technology responsible for this process is called TrueDepth. The system uses LEDs to project an irregular grid of over 30,000 infrared dots to record depth within a matter of milliseconds [21,22] and can provide a rapid, reliable, and direct method for producing 3D data [16], [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Smartphone-Based Facial Scanning as a Viable Tool for Facially Driven Orthodontics?

Thurzo¹,

Strunga²,

Havlínová³

et al. 2022

Preprint

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The current paradigm shift in orthodontic treatment planning is based on facially driven diagnostics. This requires an affordable, convenient, and non-invasive solution for face scanning. Therefore, utilization of smartphones` TrueDepth sensors is very tempting. TrueDepth refers to front-facing cameras with a dot projector in Apple devices that provide real-time depth data in addition to visual information. There are several applications that tout themselves as accurate solutions for 3D scanning of the face in dentistry. Their clinical accuracy has been uncertain. This study focuses on evaluating the accuracy of the Bellus3D Dental Pro app, which uses Apple's TrueDepth sensor. The app reconstructs a virtual, high-resolution version of the face, which is available for download as a 3D object. In this paper, sixty TrueDepth scans of the face were compared to sixty corresponding facial surfaces segmented from CBCT. Difference maps were created for each pair and evaluated in specific facial regions. The results confirmed statistically significant differences in some facial regions in amplitudes greater than 3 mm, suggesting that current technology has limited applicability for clinical use. The clinical utilization of facial scanning for orthodontic evaluation, which does not require accuracy in the lip region below 3 mm, can be considered.

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SqueezeFace: Integrative Face Recognition Methods with LiDAR Sensors

Cited by 14 publications

References 30 publications

CloudNet: A LiDAR-Based Face Anti-Spoofing Model That Is Robust Against Light Variation

CloudNet: A LiDAR-Based Face Anti-Spoofing Model That Is Robust Against Light Variation

Smartphone-Based Facial Scanning as a Viable Tool for Facially Driven Orthodontics?

Smartphone-Based Facial Scanning as a Viable Tool for Facially Driven Orthodontics?

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