This work introduces GestDoor, a novel behavioral biometric system for door access control, leveraging arm movements during door-opening actions. We compiled an extensive dataset of 3,330 samples—surpassing those in current state-of-the-art studies—with 11 participants wearing two 6-degree-of-freedom (DOF) inertial motion units (IMUs) placed on the wrist and upper arm. Each participant completed up to three data collection sessions, performing four distinct door-opening activities: left-hand pull, left-hand push, right-hand pull, and right-hand push. We extracted various temporal and frequency-domain features to assess authentication performance within sessions and permanence (performance over time). Our evaluation, using three classifiers and dynamic time warping for signal matching, shows that GestDoor achieves an impressive equal error rate (EER) range of 0.0%-0.7%, validating its potential viability as a biometric approach.

Deception detection has been extensively studied using vision-based features in domains such as crime, finance, and social interaction. However, little attention has been given to how deception manifests visually in self-reported well-being-a critical area for behavioral health, where inaccurate reporting may affect treatment outcomes and the therapeutic alliance. While clinicians often rely on visual cues such as gaze, facial expressions, and body language to assess deception, these cues remain underutilized in AI-based deception detection in wellbeing scenarios. This study explores vision-based features of deception in the well-being domain and compares them with those from three other domains: biography, academics, and crime. Using mock interview data, we extract facial landmarks, body gestures, and facial action units (AUs) using four feature selection methods. We then visualize and analyze the spatial distribution of features associated with truthful and deceptive responses. Results show that well-being features are generally fewer and more localized-particularly around the nose ridge-with unique presence of eye landmarks and limited hand gestures. In contrast, biography and academics show broader facial and body engagement, while crime displays no differentiation between truth- and deception-related features and lacks emotional AU combinations. AUs associated with joy (AU 6 and AU 12) appear consistently across well-being, academics, and biography, suggesting some domain-agnostic cues. Overall, our findings indicate that most visual features relevant to deception are domain-specific. This highlights the importance of contextaware approaches in deception modeling and supports the development of more reliable, human-centered AI tools for wellbeing assessment and mental health applications.

Brain-Computer Interface (BCI)-based authentication offers a promising alternative to traditional passwords, especially in collaborative environments where close proximity increases the risk of spoofing. Since BCI systems rely on brain signals that cannot be externally observed, they enhance security by eliminating the need for visible password inputs. We present a longitudinal study using EEG signals in a closed-loop, real-time P300-based BCI authentication system. Volunteers completed multiple login sessions over two weeks using a consistent visual stimulus pattern. We evaluate the system’s performance across three aspects: (1) how authentication scores vary over time for the same user, (2) how accurately the system verifies genuine login attempts, and (3) how well it rejects impostor attempts using the same pattern. Pearson correlation was most effective for matching the same user over time, while Chebyshev distance best distinguished between different users.

We present a first of its kind pilot study investigating distinct features presented in lip movement captured through WiFi channel state information (CSI) as four research volunteers read several emotion-charged text samples. We pursue the tasks of emotion and identity recognition with these data via feature-level fusion, extracting features from both the time and frequency domains. While the extracted frequency-domain features, i.e., zero-crossing rate and fundamental frequency, are commonly associated with audio and speech recognition related applications, we found statistical features, such as mean, median, skew, and kurtosis, most suitable for capturing salient information in CSI data. Specifically, classifying the emotional states of speakers (i.e., anger, joy, fear, love, surprise, and sadness) and the identity of speakers themselves, we achieved 96.4% and 57.9% accuracy for the identity and emotion recognition tasks, respectively.

One of the strongest predictors of success in post-secondary education is student engagement. Unfortunately, people with psychiatric disabilities are less engaged in their campus communities. This work-in-progress paper details the disclosure-based self-stigma reduction program, Up To Me, which is developed to increase inclusion and engagement of people with mental illness on college campuses by teaching strategies to weigh costs and benefits of disclosing one's mental illness. Further, we elaborate on the program's evaluation mechanisms, which involve both self-reported and passively recorded smartphone sensor data. The latter reflects a unique merging of behavioral and computer sciences that serves to facilitate behavioral modeling using artificial intelligence as an objective measure of Up to Me outcomes. Similar to data collection for some activity and biometric recognition applications, we employ a publicly available and free-to-use smartphone sensor reading app to correlate self-reported well-being with Up to Me participant behaviors. We anticipate that the behavioral data gathered via smartphones will substantiate self-report data on Up to Me outcomes.

We present a new multimodal, context-based dataset for continuous authentication. The dataset contains 27 subjects, with an age range of [8, 72], where data has been collected across multiple sessions while the subjects are watching videos meant to elicit an emotional response. Collected data includes accelerometer data, heart rate, electrodermal activity, skin temperature, and face videos. We also propose a baseline approach for fair comparisons when using the proposed dataset. The approach uses a combination of a pretrained backbone network with supervised contrastive loss for face. Time-series features are also extracted, from the physiological signals, which are used for classification. This approach, on the proposed dataset, results in an average accuracy, precision, and recall of 76.59%, 88.90, and 53.25, respectively, on electrical signals, and 90.39%, 98.77, and 75.71, respectively on face videos.

This paper presents a proof-of-concept demonstrating the feasibility of global (non-person specific) mood classification using smartphone data. We employed a publicly available dataset consisting of six weeks of phone activity data. It included call, SMS, and app events along with up to five self-reported mood entries per day for 27 subjects. While existing efforts have explored person-specific and one-vs-one mood classification models, we show that a global, multiclass mood prediction model is achievable with 65% mood classification accuracy. Our global model aims to protect the privacy of smartphone users, especially since existing research employ a mobile app to track the specific daily actions of users to infer their levels of valence and arousal. Our findings show that features representative of app, call, and text messaging patterns and previous levels of valence and arousal may be most useful for mood detection. After evaluating all features using four different feature selectors, we found that the salient feature set only resulted in a 2% degradation in performance compared to the use of all features. As our results are data-dependent, our future research will involve data collection on a much larger scale to further evaluate the feasibility of privacy-preserving mood classification. Since this work is focused on smartphone devices, our work could lead to privacy-preserving objective measurement of mood for counseling services and user-friendly mHealth applications.

Mobile device usage data such as mobile app use and acceleration measurements fluctuate often as individuals carry out their daily tasks. As these data have emerged in recent years as promising biometric identifiers, it is important to understand the many causes of these variations such that these systems can adapt without degradation in performance. In this paper, we seek to understand the impact of changes in a person's mood on the performance of a mobile biometric system using a publicly available dataset of 27 subjects. We explore the verification and identification tasks, along with mood prediction from smartphone data. We achieved an equal error rate of 3% and a d-prime value of 5.05 for the verification task, wherein experiments showed that verification is minimally influenced by an individual's mood, although negative arousal slightly degraded performance. We created a multi-class problem to study the identification task, achieving an average 83% F 1-score. Here, we observed that subjects with lower identification accuracy (95%) experienced more mood changes compared to the average. Contrasting previous claims, our findings suggest that frequent changes in mood may have little negative impact performance. Finally, positive arousal and negative valence yielded the highest area under the curve (0.67) for mood prediction. This was also the class associated with the highest average genuine and lowest average imposter scores for verification experiments, suggesting a correspondence between recognition and mood prediction tasks that applications such as sensor-enhanced mHealth apps could leverage.

Over the past decade, analyses of biometric recognition for infant and toddler identification have emerged. These efforts are critical since existing child identification programs are solely utilized in missing children cases; such programs are not employed in the wider spectrum of societal issues that child identification efforts could help to resolve, such as baby swapping in hospitals, illegal adoption, and inadequate vaccination tracking. As such, this paper provides a brief literature review on biometric recognition for infants and toddlers. We cover the range of potential applications for biometric identification of infant and toddler-aged children, along with current research findings, especially those involving fingerprint recognition due to the permanence of fingerprint features and the practicality of implementing fingerprint recognition systems for younger children. In addition, we investigate the acceptability of biometric technologies for infants and toddlers by conducting an online survey (N = 133), wherein we gather the opinions of adults on the utility of biometric systems for young children, how these systems might help solve societal issues, and problems users may face if such a system were available. Key results show that while over half of respondents are comfortable with a biometric system for infants and toddlers, and parents in particular are more likely to view biometric recognition useful for helping to resolve societal issues than non-parents, data storage, privacy, and the child's inability to provide consent on their own are common concerns.

While mobile devices are no longer a new technology, using the data generated from the use of these devices for security purposes has just recently been explored. Current methods, such as passwords, are quickly becoming antiquated, lacking the robustness, accuracy, and convenience desired to serve as reliable security measures. Since, researchers have resorted to alternative techniques, such as measurements obtained from keyboard interactions and movement, and behavioral interactions, such as application usage. However, practical implementations require further evaluation of circumvention. Thus, this work thoroughly analyzes various threats against mobile devices which use mobile device usage data as behavioral biometrics for authentication. Experimental results indicate that an outsider with a certain level of knowledge regarding the behavior of the device's owner poses a great security threat. Possible countermeasures to prevent such attacks are also provided.

Because passwords and personal identification numbers are easily forgotten, stolen, or reused on multiple accounts, the current norm for mobile device security is quickly becoming inefficient and inconvenient. Thus, manufacturers have worked to make physiological biometrics accessible to mobile device owners as improved security measures. While behavioral biometrics has yet to receive commercial attention, researchers have continued to consider these approaches as well. However, studies of interactive data are limited, and efforts which are aimed at improving the performance of such techniques remain relevant. Thus, this paper provides a performance analysis of application, Bluetooth, and Wi-Fi data collected from 189 subjects on a mobile device for user verification. Results indicate that user authentication can be achieved with up to 91% accuracy, demonstrating the effectiveness of associative classification as a feature extraction technique.