Emerging Trends in Security System Design Using the Concept of Social Behavioural Biometrics

Gavrilova, Marina L.; Ahmed, Faiz; Azam, Samiul; Paul, Padma Polash; Rahman, Wahida; Sultana, Madeena; Zohra, Fatema Tuz

doi:10.1007/978-3-319-44257-0_10

Cited by 26 publications

(11 citation statements)

References 45 publications

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“…Detecting and tracking people are very useful capabilities for different systems, in particular for improving navigation in mobile robots and also to facilitate more socially acceptable robots, but also in security applications, for instance using biometric data (Ngo et al, 2015; Gavrilova et al, 2017) or safely using robotics platforms (Morante et al, 2015). There are many solutions in the literature that try to solve this problem using a multi-modal approach, typically with vision and range sensors (Arras et al, 2012), but these kinds of approaches are very expensive both from the point of view of the cost of the sensor and the computing capabilities needed for processing and integrating, and are more likely to generate contradictory information.…”

Section: Introductionmentioning

confidence: 99%

Tracking People in a Mobile Robot From 2D LIDAR Scans Using Full Convolutional Neural Networks for Security in Cluttered Environments

et al. 2019

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Tracking people has many applications, such as security or safe use of robots. Many onboard systems are based on Laser Imaging Detection and Ranging (LIDAR) sensors. Tracking peoples' legs using only information from a 2D LIDAR scanner in a mobile robot is a challenging problem because many legs can be present in an indoor environment, there are frequent occlusions and self-occlusions, many items in the environment such as table legs or columns could resemble legs as a result of the limited information provided by two-dimensional LIDAR usually mounted at knee height in mobile robots, etc. On the other hand, LIDAR sensors are affordable in terms of the acquisition price and processing requirements. In this article, we describe a tool named PeTra based on an off-line trained full Convolutional Neural Network capable of tracking pairs of legs in a cluttered environment. We describe the characteristics of the system proposed and evaluate its accuracy using a dataset from a public repository. Results show that PeTra provides better accuracy than Leg Detector (LD), the standard solution for Robot Operating System (ROS)-based robots.

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Section: Introductionmentioning

confidence: 99%

Tracking People in a Mobile Robot From 2D LIDAR Scans Using Full Convolutional Neural Networks for Security in Cluttered Environments

et al. 2019

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“…The research on visual aesthetics presented in this paper is introduced in the context of social biometrics [5,6], an area of biometrics that understands human behavior and personality traits based on human social network activities. This emerging direction of research was preceded by the development of a new generation of biometric systems based on fundamental modeling principles [17], with a focus on the simulation of biometric information based on real-life models [18].…”

Section: Previous Workmentioning

confidence: 99%

“…While the majority of biometric research focuses on behavioral biometrics such as voice and gait [2], as well as the enhancement of accuracy through information fusion [3,4], this article presents the most comprehensive study to date on the use of aesthetic-based human traits expressed through human-machine interaction for biometric identification. In the domain of behavioral biometrics, social-behavioral biometrics utilizes a person's interpersonal interactions, dispositions, and attitudes expressed through online media and communications as features [5,6]. Social network users exhibit many unique features through daily communications [7].…”

Section: Introductionmentioning

confidence: 99%

Biometric Identification from Human Aesthetic Preferences

Sieu

Gavrilova

2020

Sensors

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In recent years, human–machine interactions encompass many avenues of life, ranging from personal communications to professional activities. This trend has allowed for person identification based on behavior rather than physical traits to emerge as a growing research domain, which spans areas such as online education, e-commerce, e-communication, and biometric security. The expression of opinions is an example of online behavior that is commonly shared through the liking of online images. Visual aesthetic is a behavioral biometric that involves using a person’s sense of fondness for images. The identification of individuals using their visual aesthetic values as discriminatory features is an emerging domain of research. This paper introduces a novel method for aesthetic feature dimensionality reduction using gene expression programming. The proposed system is capable of using a tree-based genetic approach for feature recombination. Reducing feature dimensionality improves classifier accuracy, reduces computation runtime, and minimizes required storage. The results obtained on a dataset of 200 Flickr users evaluating 40,000 images demonstrate a 95% accuracy of identity recognition based solely on users’ aesthetic preferences.

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“…On the other hand, behavioral biometric modalities focus on a person's actions, such as voice, signature, or gait rather than their physical characteristics. Within this domain, social-behavioral biometric investigates a person's identity using their social interactions and communication patterns [2]. Over the last few decades, with the flourishment of technology, the number of social media users has exploded.…”

Section: Introductionmentioning

confidence: 99%

A Novel Three Stage Framework for Person Identification From Audio Aesthetic

Iffath

Gavrilova

2022

IEEE Access

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Social behavioral biometrics investigates social interactions to determine a person's identity. Within the discipline of social behavioral biometrics, recognition of individuals based on their aesthetic preferences is an emerging direction of research. Human aesthetic is a soft, behavioral biometric trait that refers to a person's attitudes towards a particular subject material. Recent developments in aesthetic-based biometric systems have proven that an individual's visual and audio aesthetic preferences hold considerable distinctive features. This paper introduces a novel three-stage audio-aesthetic system that can uniquely identify a user from the set of their favorite songs. The system utilizes Residual Network (ResNet) for highlevel feature extraction. A hybrid meta-heuristic feature selection algorithm based on Cuckoo Search and Whale Optimization is proposed for feature extraction optimization, which results in the low-dimensional feature set. The selected subset of features is fed into the XGBoost classifier to establish a person's identity. The proposed method outperformed the handcrafted feature-based method by achieving 99.54% accuracy on a proprietary dataset (Free Music Archive) and 99.79% accuracy on a publicly available dataset (Million Playlists Dataset).INDEX TERMS Social behavioral biometrics, deep learning, biometric authentication, audio aesthetics, transfer learning, meta-heuristic, feature selection.

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Emerging Trends in Security System Design Using the Concept of Social Behavioural Biometrics

Cited by 26 publications

References 45 publications

Tracking People in a Mobile Robot From 2D LIDAR Scans Using Full Convolutional Neural Networks for Security in Cluttered Environments

Tracking People in a Mobile Robot From 2D LIDAR Scans Using Full Convolutional Neural Networks for Security in Cluttered Environments

Biometric Identification from Human Aesthetic Preferences

A Novel Three Stage Framework for Person Identification From Audio Aesthetic

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