COMPACT: Biometric Dataset of Face Images Acquired in Uncontrolled Indoor Environment

Włodarczyk, Michał; Kacperski, Damian; Sankowski, W.; Grabowski, Kamil

doi:10.7494/csci.2019.20.1.3020

Cited by 7 publications

(4 citation statements)

References 33 publications

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“…In this section we will show an overview of dataset comparable with the one proposed. COMPACT [2] is a biometric dataset focused on less-cooperative face recognition. Images are in high resolution but acquired in a fully automated manner.…”

Section: Related Workmentioning

confidence: 99%

Gotcha-I: A Multiview Human Videos Dataset

Barra

Bisogni

Nappi

et al. 2020

Communications in Computer and Information Science

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The growing need of security in large open spaces led to the need to use video capture of people in different context and illumination and with multiple biometric traits as head pose, body gait, eyes, nose, mouth, and further more. All this trait are useful for a multibiometric identification or a person re-identification in a video surveillance context. Body Worn Cameras (BWCs) are used by the police of various countries all around the word and their use is growing significantly. This raises the need to test new recognition methods that use multibiometric traits on person re-identification. The purpose of this work is to present a new video dataset called Gotcha-I. This dataset has been obtained using more mobile cameras in movement to adhere to the data of BWCs. The dataset includes videos from 62 subjects in indoor and outdoor environment to address both security and surveillance problem. Subjects have different behavior in videos including walk freely, follow a path, go upstairs, try to avoid the camera, for a total of 493 videos including a 180 degree videos for each face of the subjects in the dataset. Furthermore, there are already processed data, such as: the 3D model of the face of each subject with all the poses of the head in pitch, yaw and roll; and the body keypoint coordinates of the gait for each video frame. It's also shown an application of gender recognition performed on Gotcha-I, confirming the usefulness and innovativeness of the proposed dataset.

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Section: Related Workmentioning

confidence: 99%

Gotcha-I: A Multiview Human Videos Dataset

Barra

Bisogni

Nappi

et al. 2020

Communications in Computer and Information Science

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“…In the need of non-intrusive bio-metric measurements [1], Face Recognition (FR) comes as a pioneer. It is the main player in domains like entertainment (e.g., virtual reality), surveillance (e.g., identification) and security (e.g., banking).…”

Section: Introductionmentioning

confidence: 99%

3d Deformation Signature for Dynamic Face Recognition

Shabayek

Aouada

Cherenkova

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This work proposes a novel 3D Deformation Signature (3DS) to represent a 3D deformation signal for 3D Dynamic Face Recognition. 3DS is computed given a non-linear 6D-space representation which guarantees physically plausible 3D deformations. A unique deformation indicator is computed per triangle in a triangulated mesh as a ratio derived from scale and in-plane deformation in the canonical space. These indicators, concatenated, construct the 3DS for each temporal instance. There is a pressing need of non-intrusive bio-metric measurements in domains like surveillance and security. By construction, 3DS is a non-intrusive facial measurement that is resistant to common security attacks like presentation, template and adversarial attacks. Two dynamic datasets (BU4DFE and COMA) were examined, in a standard classification framework, to evaluate 3DS. A first rank recognition accuracy of 99.9%, that outperforms existing literature, was achieved. Assuming an open-world setting, 99.97% accuracy was attained in detecting unseen distractors.

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“…Human activity recognition (HAR) has revolutionized the area of computer vision research in a wide spectrum of applications [3]. Systems based on HAR enable among others the implementation of tasks related to recognizing life threatening situations [4], preventing crime and vandalism [5,6], supervision of the sick and elderly [7], biometric face identification [8][9][10][11] and analysis and classification of all forms of human activity that may be of interest in a given situation [12][13][14][15][16][17]. To achieve full efficiency, it is required to develop optimal decision algorithms.…”

Section: Introductionmentioning

confidence: 99%

Hybrid imaging-AI approach for handling critical situations in a fast-changing environment: preliminary study

Surówka

2020

MATEC Web Conf.

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The purpose of this study is to explore the possibility of using selected imaging technologies in automated video surveillance systems. The main goal of this project is to handle events that may lead to security risks, injuries, etc in various environments without relaying on more conventional sensors such as infrared photocells. For this purpose it is necessary to perform a thorough analysis of the events to be interpreted as situations of interest. It is also important to consider the hardware requirements and restrictions for developing such system. The project requires defining a hardware as well as software platform(s) and their integration into an automated tool. This paper describes the implementation of the famous Microsoft Kinect 2.0 depth sensor (well known in gaming and recreational applications) for shape/skeleton detection, and its integration into an artificial intelligence based platform utilizing selected machine learning methods. The author reveals the system implementation details, and then demonstrates its shape detection capabilities while in operation.

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COMPACT: Biometric Dataset of Face Images Acquired in Uncontrolled Indoor Environment

Cited by 7 publications

References 33 publications

Gotcha-I: A Multiview Human Videos Dataset

Gotcha-I: A Multiview Human Videos Dataset

3d Deformation Signature for Dynamic Face Recognition

Hybrid imaging-AI approach for handling critical situations in a fast-changing environment: preliminary study

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