3D Face Recognition

Ardabilian, Mohsen; Szeptycki, Przemyslaw; Huang, Di; Chen, Liming

doi:10.1002/9781118561911.ch5

Cited by 3 publications

(1 citation statement)

References 50 publications

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“…Fingerprint unlocking based on capacitance sensing principle [6][7][8][9] is more attractive than other technologies such as face, [10][11][12][13][14] iris, and [15][16][17][18] ultrasonics [19][20][21][22][23] types. Unfortunately, the contacts of fingertip with capacitance sensor module cause ESD damage easily, reducing the sensor lifetime and worsening the whole product reliability.…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of pixel‐based adjustable ESD protection circuits for capacitive fingerprint biometric sensors

Lin

Hsieh

Chang

2022

Circuit Theory & Apps

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The trade-off design between ESD endurability and polymer thickness on sensor plate for capacitive fingerprint is emerging in these years since the image quality of fingerprint is vital for biometrics authentication. In this paper, we propose a pixel-based ESD protection circuitry in parallel to GDPMOS and GGNMOS and an adjustable body terminal in proposed pixel-based ESD circuit to meet and exceed air discharge ±15 kV without thicker polymer. With the proposed pixel-based adjustable ESD protection circuit, the maximum air discharge achieved by (18 kV, À17 kV), and 10 out of 10 samples passed +15 kV, and 10 out of 10 samples passed À15 kV. Compared to conventional ground wall fingerprint sensor, the maximum air discharge was stopped by (15 kV, À15 kV), and just merely five out 10 samples passed 15 kV, seven out of 10 samples passed À15 kV. Besides the excellent ESD endurability, the proposed pixel-based ESD protection circuit achieves air-discharge 15.5 kV and grayscale count 230 at merely 50 μm thickness of polymer. Compared to the conventionally used ground wall sensor types, it passes only 13 kV and a grayscale count of 210 at a polymer thickness of 50 μm, but fails 15 KV Level 4 of IEC-61000-4-2. Even with polymer thickness increased to 250 μm, ground wall sensor types barely passed 15 kV, and the grayscale count dropped to 120, which is far from PIV image standard.

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Section: Introductionmentioning

confidence: 99%

Design and implementation of pixel‐based adjustable ESD protection circuits for capacitive fingerprint biometric sensors

Lin

Hsieh

Chang

2022

Circuit Theory & Apps

View full text Add to dashboard Cite

show abstract

Three-dimensional (3D) facial recognition and prediction

Okuwobi

Chen

Niu

et al. 2016

SIViP

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3D Face Recognition Based on Regional Shape Maps

Xiaoni¹

2018

JACIII

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This study proposes an iterative closest shape point (ICSP) registration method based on regional shape maps for 3D face recognition. A neutral expression image randomly selected from a face database is considered as the reference face. The point-to-point correspondences between the input face and the reference face are achieved by constructing the points’ regional shape maps. The distance between corresponding point pairs is then minimized by iterating through the correspondence findings and coordinate transformations. The vectors composed of the closest shape points obtained in the last iteration are regarded as the feature vectors of the input face. These 3D face feature vectors are finally used for both training and recognition using the Fisherface method. Experiments are conducted using the 3D face database maintained by the Chinese Academy of Science Institute of Automation (CASIA). The results show that the proposed method can effectively improve 3D face recognition performance.

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3D Face Recognition

Cited by 3 publications

References 50 publications

Design and implementation of pixel‐based adjustable ESD protection circuits for capacitive fingerprint biometric sensors

Design and implementation of pixel‐based adjustable ESD protection circuits for capacitive fingerprint biometric sensors

Three-dimensional (3D) facial recognition and prediction

3D Face Recognition Based on Regional Shape Maps

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