An Overview of Biometrics and Face Spoofing Detection

Sudeep, Sista Venkata Naga Veerabhadra Sai; Kiran, Siripuri; Nandan, Durgesh; Kumar, Sanjeev

doi:10.1007/978-981-15-7961-5_82

Cited by 23 publications

(27 citation statements)

References 15 publications

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“…This high rate of success is most likely due to the difference in material and reflectivity between skin and mask which induce different noise characteristics (see Section 2.2 ). Determining real from masked faces can be exploited in detecting spoofing attacks [ 38 , 39 , 40 ].…”

Section: Distinguishing Real From Fake Faces Using Sensor Noisementioning

confidence: 99%

What Can We Learn from Depth Camera Sensor Noise?

Haider

Hel‐Or

2022

Sensors

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Although camera and sensor noise are often disregarded, assumed negligible or dealt with in the context of denoising, in this paper we show that significant information can actually be deduced from camera noise about the captured scene and the objects within it. Specifically, we deal with depth cameras and their noise patterns. We show that from sensor noise alone, the object’s depth and location in the scene can be deduced. Sensor noise can indicate the source camera type, and within a camera type the specific device used to acquire the images. Furthermore, we show that noise distribution on surfaces provides information about the light direction within the scene as well as allows to distinguish between real and masked faces. Finally, we show that the size of depth shadows (missing depth data) is a function of the object’s distance from the background, its distance from the camera and the object’s size. Hence, can be used to authenticate objects location in the scene. This paper provides tools and insights into what can be learned from depth camera sensor noise.

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Section: Distinguishing Real From Fake Faces Using Sensor Noisementioning

confidence: 99%

What Can We Learn from Depth Camera Sensor Noise?

Haider

Hel‐Or

2022

Sensors

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“…Partitioning, on the other hand, is based on pixel grouping based on their similarity to one another. The most crucial aspect of image processing is image segmentation [3,4]. Dividing an image into many sections gives it more significance and makes it easier to process.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of Image Segmentation Methods

Lakshmi

Kumar

2022

Advances in Transdisciplinary Engineering

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Image segmentation is a crucial stage in image processing because it allows for meaningful image analysis and information extraction. Image segmentation may be done in a variety of ways. This study examines a few of these approaches for grayscale images. MATLAB 8.1 was used to construct the paper. In order to analyze and extract information from these pictures, segmentation is essential. The segmentation of images has a promising future and has been the focus of modern study. The usual segmentation methods were addressed here: edge detection, clustering, and region growth. The problem with segmentation is that no one approach is suited for a certain type of picture, nor are all techniques applicable to all images.

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“…Biometric authentication is basically biometric-based authentication. Biometrics can be used in criminal investigations, security and educational settings, and countless other places [6][7][8][9]. The most commonly used biometric strategies include iris, palm-print, voice, and a unique impression of the finger.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of Advanced Technology for Fingerprint Detection System

Nandana

Sankar

Singh

2022

Advances in Transdisciplinary Engineering

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Current research on fingerprint recognition is discussed in the following article. Focuses on earlier investigations of fingerprint recognition systems and a brief review of fingerprint recognition’s conceptual and structural frameworks are presented in this paper The fundamentals of fingerprint recognition have been explained. A pre-processing phase is used to eliminate irrelevant data and enhance the visibility of added character by employing an enhancement technique for biometric data acquisition. An additional stage extracts fingerprint features from the output of the pre-processing phase. The database template is compared to the newly obtained feature. This study aims to evaluate recent work on fingerprint recognition systems and elucidate fingerprint recognition steps gradually and present descriptions of fingerprint records with features.

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An Overview of Biometrics and Face Spoofing Detection

Cited by 23 publications

References 15 publications

What Can We Learn from Depth Camera Sensor Noise?

What Can We Learn from Depth Camera Sensor Noise?

An Analysis of Image Segmentation Methods

An Analysis of Advanced Technology for Fingerprint Detection System

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