Gait Analysis for Gender Classification in Forensics

Barra, Paola; Bisogni, Carmen; Nappi, Michele; Freire-Obregón, David; Castrillón-Santana, Modesto

doi:10.1007/978-981-15-1304-6_15

Cited by 19 publications

(11 citation statements)

References 27 publications

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“…Once a suspicious behaviour is detected, Pepper can be alerted and go in the same room of the camera to check at short distance the identity of the subject. At the same time, soft biometric traits can be detected to perform an initial subject identification from traits like the gender [25] or facial attributes [26]. Those data have a threefold function: to help to identify a user before the involvement of Pepper; to detect a malicious intent also in low-resolution data; to furnish information to increase or decrease the trustworthiness of a subject.…”

Section: B Methods Built On Smart Camerasmentioning

confidence: 99%

Contextual Trust Model With a Humanoid Robot Defense for Attacks to Smart Eco-Systems

et al. 2020

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Pepper is a humanoid robot that just embeds the few computational resources for controlling its sensors and actuators, and is not capable of handling big amounts of data or performing in parallel complicate tasks. Aiming at enriching its functionalities and its interaction with the environment, the robot has been put in communication with a plethora of satellite smart objects and services ranging from simple environmental sensors, up to deep learning enhanced smart cameras. The addition of biometric, emotional, social, machine learning and other capabilities to Pepper, while enabling advanced functionalities and additional instruments for controlling users and the environment, raises security and, obviously, privacy concerns. The robot itself, its interaction with the environment and every weakness exposed by the smart objects involved in its ecosystem, may represent an exploit point for attacking the smart home and threaten security and privacy. Aiming at preventing attacks and strengthen security, each action with the system is evaluated against the entire context, as detected by the entire eco-system of smart-objects. This paper describes and analyses the experience and how the semantic trust model adopted mitigates the effects of weaknesses and the risks related to smart home cyber-attacks.INDEX TERMS Social robots, human-robot interaction, Internet of Things, ontologies, trust model, Pepper.

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Section: B Methods Built On Smart Camerasmentioning

confidence: 99%

Contextual Trust Model With a Humanoid Robot Defense for Attacks to Smart Eco-Systems

et al. 2020

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“…Various methodologies have been adopted to discriminate users; for example, distance measurements, such as Euclidean distance or the Manhattan distance, have been used to evaluate the similarity between acquired feature data and prebuilt user templates [26][27][28]. Modern, advanced computers have enabled conventional analytic methods with different distance metrics to be replaced by machine learning algorithms for biometric identification [17,18,20,32].…”

Section: Related Workmentioning

confidence: 99%

“…Behavioural authentication systems recognize behavioural biometrics; these are patterns of actions performed by the users such as keystroke and touch dynamics (typing and touchscreen usage patterns) [14][15][16][17][18], signature, and gait (walking patterns) [19,20]. Authentication is achieved by first recording the user's behaviours over a period of time, then constructing a specific model corresponding to the recorded behaviours, and finally comparing the created model and the newly observed user behaviours to determine whether a user is an authorized user.…”

Section: Introductionmentioning

confidence: 99%

Framework for user behavioural biometric identification using a multimodal scheme with keystroke trajectory feature and recurrent neural network on a mobile platform

Tse¹,

Hung²

2022

IET Biometrics

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Diverse applications are used on mobile devices. Because of the increasing dependence on information systems, immense amounts of personal and sensitive data are stored on mobile devices. Thus, security or privacy breaches are a major challenge. To protect mobile systems and the private information on these systems from being accessed by adversaries, a framework for mobile user identification through the use of a multimodal behavioural biometrics scheme with a keystroke trajectory feature is presented herein. Conventionally, mobile devices have been protected by mechanisms such as PINs or passwords. However, these approaches have numerous disadvantages. Therefore, approaches that employ keystroke biometrics for secure and reliable mobile device identification have been proposed. Because unimodal behavioural biometrics identification mechanisms have limited accuracy and effectiveness, a multimodal scheme that includes different behavioural biometric traits, such as keystroke and swipe biometric traits, is examined. However, the information provided by the spatial and temporal features of keystroke biometrics is not comprehensive. Therefore, a trajectory model is derived to describe the behavioural biometric uniqueness of a user. In the user identification phase, a multistream recurrent neural network (RNN) is adopted. The results reveal that the proposed trajectory model performs well, and the multimodal scheme using an RNN with a late fusion method provides accurate identification results. The proposed system achieved an accuracy of 95.29%, F1 score of 94.64%, and equal error rate of 1.78%. Thus, the proposed mobile identification system is capable of resisting attacks that standard mechanisms may be vulnerable to and represents a valuable contribution to cyber security.

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“…Third, the proposed compound scaling of EfficientPose assumes that the scaling relationship between resolution, width, and depth, as defined by (2), is identical in HPE and image classification. However, the optimal compound scaling coefficients might be different for HPE, where the precision level is more dependent on image resolution, than for image classification.…”

Section: Avenues For Further Researchmentioning

confidence: 99%

“…Moreover, by spending 160 billion floating-point operations (GFLOPs) per inference, OpenPose is considered highly inefficient. Despite these issues, OpenPose seems to remain a commonly applied network for single-person HPE performing markerless motion capture from which critical decisions are based upon [2,56].…”

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confidence: 99%

EfficientPose: Scalable single-person pose estimation

2020

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Single-person human pose estimation facilitates markerless movement analysis in sports, as well as in clinical applications. Still, state-of-the-art models for human pose estimation generally do not meet the requirements of real-life applications. The proliferation of deep learning techniques has resulted in the development of many advanced approaches. However, with the progresses in the field, more complex and inefficient models have also been introduced, which have caused tremendous increases in computational demands. To cope with these complexity and inefficiency challenges, we propose a novel convolutional neural network architecture, called EfficientPose, which exploits recently proposed EfficientNets in order to deliver efficient and scalable single-person pose estimation. EfficientPose is a family of models harnessing an effective multi-scale feature extractor and computationally efficient detection blocks using mobile inverted bottleneck convolutions, while at the same time ensuring that the precision of the pose configurations is still improved. Due to its low complexity and efficiency, EfficientPose enables real-world applications on edge devices by limiting the memory footprint and computational cost. The results from our experiments, using the challenging MPII single-person benchmark, show that the proposed EfficientPose models substantially outperform the widely-used OpenPose model both in terms of accuracy and computational efficiency. In particular, our top-performing model achieves state-of-the-art accuracy on single-person MPII, with low-complexity ConvNets.

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Gait Analysis for Gender Classification in Forensics

Cited by 19 publications

References 27 publications

Contextual Trust Model With a Humanoid Robot Defense for Attacks to Smart Eco-Systems

Contextual Trust Model With a Humanoid Robot Defense for Attacks to Smart Eco-Systems

Framework for user behavioural biometric identification using a multimodal scheme with keystroke trajectory feature and recurrent neural network on a mobile platform

EfficientPose: Scalable single-person pose estimation

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