A long-distance 3D face recognition architecture utilizing MEMS-based region-scanning LiDAR

Lim, Yeerang; Lee, Sung Hyun; Cheong, Sungjin; Park, Yong-Hwa

doi:10.1117/12.2649500

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…Additionally, the LiDAR utilizes the region-scanning method to obtain 3D data with flexible Field-of-View (FoV) and various resolutions. This allows the sensor to control a wide FoV for whole scene or narrow FoV for specific parts to obtain 3D data 11 . The optical structure and configuration of the proposed LiDAR is shown in The object scanning process obtains the region of the target from an amplitude image from region-scanning LiDAR.…”

Section: Object Scanning Processmentioning

confidence: 99%

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A long-distance 6D pose estimation system for space object recognition using region-scanning LiDAR

Lim,

An,

Lee

et al. 2024

MOEMS and Miniaturized Systems XXIII

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In this paper, a novel 6-Dimensional (3 positions and 3 orientations) pose estimation system using indirect Time-of-Flight (ToF) region-scanning LiDAR is proposed for long-distance space object recognition. Specifically, the targeted space object detection algorithm with an IR amplitude image, and 6D pose estimation algorithm with high-resolution 3D data are performed with the region-scanning LiDAR. The proposed system is verified with a self-collected dataset of space objects in space simulation environments. The proposed pose estimation algorithm shows a maximum position error of 1% and orientation error of 5° in robust experiment conditions. Moreover, the proposed system outperformed the other conventional space object 6D pose estimation system with previous depth sensors in terms of the detection rates and 6D pose accuracy at long distances.

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Section: Object Scanning Processmentioning

confidence: 99%

“…The 3D-GCN network, a graph-based network using point clouds, extracts a global feature vector. The 3D-GCN network uses a spatial vector kernel to better extract the information inherent in 3D data 11 .…”

Section: D Pose Estimation Processmentioning

confidence: 99%

A long-distance 6D pose estimation system for space object recognition using region-scanning LiDAR

Lim,

An,

Lee

et al. 2024

MOEMS and Miniaturized Systems XXIII

Self Cite

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“…Computer vision and deep learning have made significant progress in many fields in the past few years, including object detection [11][12][13][14], face recognition [15][16][17][18][19], and pose estimation [20][21][22][23]. These successful applications provide insights for the research in other fields.…”

Section: Introductionmentioning

confidence: 99%

Massage Acupoint Positioning Method of Human Body Images Based on Transfer Learning

Zhang,

Wu,

Wang

et al. 2023

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Traditional Chinese massage therapy is a very popular method to stay healthy, which regulates body balance, alleviates fatigue, and prevents diseases by massaging specific acupoints. Although computer vision has been increasingly applied in traditional Chinese medicine, related study of acupoint positioning is still insufficient. The existing acupoint positioning methods mainly rely on manual labeling and rule matching, which often require a large amount of manual intervention with limited accuracy. Therefore, this study proposed a massage acupoint positioning method of human body images based on transfer learning. The massage acupoint meridian and collateral positioning principle of human body images was presented. Using the integrated deep belief network model as a pre-trained model, a feasible transfer learning model was established through fine-tuning and feature mapping. The experimental results verified that the proposed method was effective. Relevant research results provide useful references for research in related fields.

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A Comparison of Transfer Learning Models for Face Recognition

Alashammari,

Akgün

2024

SAUCIS

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Face recognition (FR) is a method that uses face feature analysis and comparison to identify or verify individuals. Siamese neural networks (SNNs) are an effective method for FR, providing high accuracy and versatility, especially in situations where data is restricted. Unlike standard neural networks, SNNs learn to distinguish between pairs of inputs rather than individual inputs. However, detecting and recognizing faces in unconstrained environments poses a significant challenge due to various factors such as head pose, illumination, and facial expression variations. The aim of this paper is to design and develop an efficient approach based on SNNs and Transfer Learning methods. For this purpose LFW dataset and transfer learning architectures like VGG-16, EfficientNet, RestNet50 and ConvNext have been utilised. Performance of the architectures were measured using 5-Fold cross validation. According to results, EfficientNet, RestNet50 and ConvNext produced 78% accuracy, 95% and 93 % accuracy respectively. SNN with VGG-16 exhibited a low loss and produced the best accuracy in face recognition with 96%.

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A long-distance 3D face recognition architecture utilizing MEMS-based region-scanning LiDAR

Cited by 3 publications

References 9 publications

A long-distance 6D pose estimation system for space object recognition using region-scanning LiDAR

A long-distance 6D pose estimation system for space object recognition using region-scanning LiDAR

Massage Acupoint Positioning Method of Human Body Images Based on Transfer Learning

A Comparison of Transfer Learning Models for Face Recognition

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