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Body keypoints extracted by OpenPose.
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Gender Classification (GC) is a natural ability that belongs to the human beings. Recent improvements in computer vision provide the possibility to extract information for different classification/recognition purposes. Gender is a soft biometrics useful in video surveillance, especially in uncontrolled contexts such as low-light environments, with...
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... In previous studies, gender prediction on smart devices often relied on voice recordings and facial images [24], [25]. Furthermore, studies in the literature explore age and gender estimation using image-based gait information [26]- [28]. For instance, the authors investigated the application of computer vision and gait analysis in gender classification for forensics in [28]. ...
... Furthermore, studies in the literature explore age and gender estimation using image-based gait information [26]- [28]. For instance, the authors investigated the application of computer vision and gait analysis in gender classification for forensics in [28]. The study employed video sequences captured through various modalities. ...
Smart devices equipped with various sensors enable the acquisition of users’ behavioral biometrics. These sensor data capture variations in users’ interactions with the devices, which can be analyzed to extract valuable information such as user activity, age group, and gender. In this study, we investigate the feasibility of using gait data for gender detection of users. To achieve this, we propose a novel gender detection scheme based on a deep learning approach, incorporating synthetic data generation and continuous wavelet transform (CWT). In this scheme, the real dataset is first divided into training and test datasets, and then synthetic data are intelligently generated using various techniques to augment the existing training data. Subsequently, CWT is used as the feature extraction module, and its outputs are fed into a deep learning model to detect the gender of users. Different deep learning models, including convolutional neural network (CNN) and long short-term memory (LSTM), are employed in classification. Consequently, we evaluate our proposed framework on different publicly available datasets. On the BOUN Sensor dataset, we obtain an accuracy of 94.83%, marking a substantial 6.5% enhancement over the prior highest rate of 88.33%. Additionally, we achieve 86.27% and 88.15% accuracy on the OU-ISIR Android and OU-ISIR Center IMUZ datasets, respectively. Our experimental results demonstrate that our proposed model achieves high detection rates and outperforms previous methods across all datasets.
... The possibility of having targeted advertising depending on the user, an easier interaction with a certain hardware, or the fusion with other information for a better identification or verification of a subject, are just some of the potential applications that could benefit from an ad-hoc analysis of behavioural data from mobile devices. The extraction of such soft information can be achieved by exploiting various physical or behavioural traits such as pupil size [9,10,11] , 2D human skeleton [12] , and many others, with convincing performance. Talking about the use of smartphones and similar portable devices, the keystroke dynamics is the elective data stream, coming from the interaction with the device, that could be used to infer demographic information. ...
Soft biometric traits are not fully distinctive in recognition tasks, but they can effectively added to biometric recognition systems to improve the overall performance. In this work, the focus is on the analysis of touch keystroke dynamics of smartphone’s users for demographic classification in age, gender and user experience. Starting from the data collected in three publicly available datasets and using traditional lightweight machine learning classification algorithms, the results reported in this work shows that an effective demographic analysis can be achieved as well as continuous authentication could be improved. Moreover, the study emphasize a critical issue affecting the experimental protocols in soft biometric analysis, discussing how sensibly the performance of a system can increase on a not wise splitting of the samples in the datasets.
... 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. ...
... 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]. ...
Abstract 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.
... Behavioural biometrics tell more about the way of acting and living of an individual. Handwriting, speech tone, keystroke dynamics, gait [2] are some of the most common biometrics. All those aspects are strongly related to the habits and the psychology of an individual and, as a consequence, are subject to change over time since each of us tends to slightly modify our behaviour and habits. ...
Web user behavioral recognition is the process by which web users are identified and distinguished through behavioral features. In this work, two sources of behavioral biometric data are analyzed for the development of this web user identification model, touch dynamics and the characteristics extracted from the periocular area related to the pupils, blinks and fixations. The approach adopted used to improve the overall performance of the multimodal biometric recognition system is based on a fusion at the Feature level to which different distance measure techniques (Euclidean, Bray-Curtis, Manhattan, Canberra, Chebyshev, Cosine) are applied to determine if the test sample belongs to the target subject. To further improve the system performance, we have applied multi-data processing methods such as Canonical Correlation Analysis (CCA) and Principal Component Analysis (PCA). The results obtained demonstrate the promise of these two different biometric traits and, above all, of their fusion. In fact, the fusion approach allows obtaining an accuracy higher than that of individual biometrics, reaching an accuracy of over 92%.
... 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]. ...
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.
... For the age estimation, different approaches are provided in literature. One of them is the gait analysis [19]. In this work, we used pre-trained models which make use of CNNs [23] [22] for both age estimation and ethnicity classification. ...
... Several studies in the literature on exercise evaluation have used machine learning methods to classify individual repetitions into correct or incorrect motion classes. The methods used for this purpose include the Adaboost classifier, the closest neighbors k, the Bayesian classifier, and a set of multi-layered perceptron NNs [27]. The outputs in these approaches are discrete class values of 0 or 1 (that is, incorrect or correct repetition). ...
Human kinetics are of major medical interest especially in case of disabled persons. Rehabilitation physicians and patients can be aided by bioinformatics and modeling techniques into tailoring their efforts. In this paper we are going to review computer simulation environments and sensor measurements in motion dynamics; them being used in rehabilitation procedures.
... However, it is also possible to use videos in which subjects can move freely, also far from camera, using the gait as biometric traits [24] [25] or data collected by mobile devices [26]. It is also interesting how different results are if we consider the cooperativeness or the non cooperativeness of the subjects, labeled in this dataset, as the work in [27]. Using the face or the movement of the subject we can also extract information about their age, as in [29] [30]. ...
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 these traits 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 different countries all around the word and their use is growing significantly. This raises the need to develop new recognition methods that consider 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 to adhere to the data of BWCs. The dataset includes videos from 62 subjects in indoor and outdoor environments to address both security and surveillance problem. During these videos, subjects may have a different behavior in videos such as freely, path, upstairs, avoid the camera. The dataset is composed by 493 videos including a set of 180\(^\circ \) 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.
... 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. ...
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 eco-system, 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.
