3D Multi-Spectrum Sensor System with Face Recognition

Kim, Joongrock; Yu, Sunjin; Kim, Ig-Jae; Lee, Sangyoun

doi:10.3390/s131012804

Cited by 7 publications

(8 citation statements)

References 47 publications

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“…In addition to the use of standard 2D cameras, existing solutions for reliable face recognition include the use of multisensor approaches [ 7 ] as well as the use of specialized sensors such as 3D sensors [ 8 ], infrared assisted sensors such as FaceID [ 9 ] and Kinect [ 10 ], long-range sensors such as FaceSentinel [ 11 ], recent behind-the-screen sensors [ 12 ], thermal sensors [ 13 ], smart glasses [ 14 ] or multi-view sensors [ 15 ], to name a few. The main drawback of using special sensor hardware is its price, which may be prohibitive for many applications, while some laser scanner devices can also be harmful to human eyes, making them unsuitable for face recognition.…”

Section: Introductionmentioning

confidence: 99%

Making the Most of Single Sensor Information: A Novel Fusion Approach for 3D Face Recognition Using Region Covariance Descriptors and Gaussian Mixture Models

Krizaj

Dobrišek

2022

Sensors

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Most commercially successful face recognition systems combine information from multiple sensors (2D and 3D, visible light and infrared, etc.) to achieve reliable recognition in various environments. When only a single sensor is available, the robustness as well as efficacy of the recognition process suffer. In this paper, we focus on face recognition using images captured by a single 3D sensor and propose a method based on the use of region covariance matrixes and Gaussian mixture models (GMMs). All steps of the proposed framework are automated, and no metadata, such as pre-annotated eye, nose, or mouth positions is required, while only a very simple clustering-based face detection is performed. The framework computes a set of region covariance descriptors from local regions of different face image representations and then uses the unscented transform to derive low-dimensional feature vectors, which are finally modeled by GMMs. In the last step, a support vector machine classification scheme is used to make a decision about the identity of the input 3D facial image. The proposed framework has several desirable characteristics, such as an inherent mechanism for data fusion/integration (through the region covariance matrixes), the ability to explore facial images at different levels of locality, and the ability to integrate a domain-specific prior knowledge into the modeling procedure. Several normalization techniques are incorporated into the proposed framework to further improve performance. Extensive experiments are performed on three prominent databases (FRGC v2, CASIA, and UMB-DB) yielding competitive results.

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Section: Introductionmentioning

confidence: 99%

Making the Most of Single Sensor Information: A Novel Fusion Approach for 3D Face Recognition Using Region Covariance Descriptors and Gaussian Mixture Models

Krizaj

Dobrišek

2022

Sensors

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“…The integration of different imaging modalities into one system yields the so-called multimodal imaging. In recent years, a lot of multimodal imaging systems [ 16 , 17 , 18 , 19 , 20 ] combining at least two imaging modalities such as 3D imaging, multispectral imaging, thermal imaging, etc. were developed for different applications in biometrics, culture heritage, biomedicine, and industry.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Contactless Vital Sign Estimation from Real-Time Multimodal 3D Image Data

et al. 2020

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The contactless estimation of vital signs using conventional color cameras and ambient light can be affected by motion artifacts and changes in ambient light. On both these problems, a multimodal 3D imaging system with an irritation-free controlled illumination was developed in this work. In this system, real-time 3D imaging was combined with multispectral and thermal imaging. Based on 3D image data, an efficient method was developed for the compensation of head motions, and novel approaches based on the use of 3D regions of interest were proposed for the estimation of various vital signs from multispectral and thermal video data. The developed imaging system and algorithms were demonstrated with test subjects, delivering a proof-of-concept.

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“…To overcome the limitations of the token-based and knowledge-based methods, biometric-based methods are increasingly being used as an alternative using the information from several physical and/or behavioral characteristics of people as the key (or password) to protect the personal information [ 1 ]. Many biometric features have been used in applications such as fingerprint [ 4 , 5 ], face [ 6 , 7 ], finger-vein [ 8 , 9 , 10 ], palm-vein [ 11 , 12 ] and iris [ 13 , 14 , 15 ]. The use of biometric features offers several advantages over the token-based and knowledge-based methods.…”

Section: Introductionmentioning

confidence: 99%

“…Although the biometric-based methods have proven efficient for authentication with high recognition rate [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ], they still have their limitations. There are two main problems with a biometric system: the effect of image capturing condition and the potential for spoofing attacks.…”

Section: Introductionmentioning

confidence: 99%

Spoof Detection for Finger-Vein Recognition System Using NIR Camera

Nguyen

Yoon

Pham

et al. 2017

Sensors

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Finger-vein recognition, a new and advanced biometrics recognition method, is attracting the attention of researchers because of its advantages such as high recognition performance and lesser likelihood of theft and inaccuracies occurring on account of skin condition defects. However, as reported by previous researchers, it is possible to attack a finger-vein recognition system by using presentation attack (fake) finger-vein images. As a result, spoof detection, named as presentation attack detection (PAD), is necessary in such recognition systems. Previous attempts to establish PAD methods primarily focused on designing feature extractors by hand (handcrafted feature extractor) based on the observations of the researchers about the difference between real (live) and presentation attack finger-vein images. Therefore, the detection performance was limited. Recently, the deep learning framework has been successfully applied in computer vision and delivered superior results compared to traditional handcrafted methods on various computer vision applications such as image-based face recognition, gender recognition and image classification. In this paper, we propose a PAD method for near-infrared (NIR) camera-based finger-vein recognition system using convolutional neural network (CNN) to enhance the detection ability of previous handcrafted methods. Using the CNN method, we can derive a more suitable feature extractor for PAD than the other handcrafted methods using a training procedure. We further process the extracted image features to enhance the presentation attack finger-vein image detection ability of the CNN method using principal component analysis method (PCA) for dimensionality reduction of feature space and support vector machine (SVM) for classification. Through extensive experimental results, we confirm that our proposed method is adequate for presentation attack finger-vein image detection and it can deliver superior detection results compared to CNN-based methods and other previous handcrafted methods.

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3D Multi-Spectrum Sensor System with Face Recognition

Cited by 7 publications

References 47 publications

Making the Most of Single Sensor Information: A Novel Fusion Approach for 3D Face Recognition Using Region Covariance Descriptors and Gaussian Mixture Models

Making the Most of Single Sensor Information: A Novel Fusion Approach for 3D Face Recognition Using Region Covariance Descriptors and Gaussian Mixture Models

Enhanced Contactless Vital Sign Estimation from Real-Time Multimodal 3D Image Data

Spoof Detection for Finger-Vein Recognition System Using NIR Camera

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