Team Faultline Measures: Rescaling the Weights of Diversity Attributes

Faultline, or subgroup formation based on the alignment of diversity attributes, can cause conflicts and low coordination in diverse teams. While researchers understand the importance of faultlines in team process and negotiations, current computational faultline measures are highly vulnerable to subjective weight assignment of diversity attributes. Therefore, there is limited understanding of which diversity attributes have more impact on faultline formation. In this paper we report 1) a pilot study illustrating the susceptibility of the current faultline measures to subjective evaluations, and 2) an online study illustrating how people’s surface (e.g. age, gender, race) and deep (e.g. personality, cultural norms) level diversity attributes impact their preference and selection of team members, as a proxy of faultline formation. We find while various surface and deep-level attributes predict selection of members, most of these attributes are highly correlated with members’ age, suggesting the importance of this attribute. We discuss future directions for faultline measures with objective rescaling of diversity weights

Team Faultline Measures: The Effect of Rescaling Weights

Faultline, or subgroup formation based on the alignment of diversity attributes, can cause conflicts and low coordination in diverse teams. While researchers understand the importance of faultlines in team process and negotiations, current computational faultline measures are highly vulnerable to subjective weight assignment of diversity attributes. Therefore, there is limited understanding of which diversity attributes have more impact on faultline formation. In this paper we report 1) a pilot study illustrating the susceptibility of the current faultline measures to subjective evaluations, and 2) an online study illustrating how people’s surface (e.g. age, gender, race) and deep (e.g. personality, cultural norms) level diversity attributes impact their preference and selection of team members, as a proxy of faultline formation. We find while various surface and deep-level attributes predict selection of members, most of these attributes are highly correlated with members’ age, suggesting the importance of this attribute. We discuss future directions for faultline measures with objective rescaling of diversity weights

Generalizability and Application of the Skin Reflectance Estimate Based on Dichromatic Separation (SREDS)

Face recognition (FR) systems have become widely used and readily available in recent history. However, differential performance between certain demographics has been identified within popular FR models. Skin tone differences between demographics can be one of the factors contributing to the differential performance observed in face recognition models. Skin tone metrics provide an alternative to self-reported race labels when such labels are lacking or completely not available e.g. large-scale face recognition datasets. In this work, we provide a further analysis of the generalizability of the Skin Reflectance Estimate based on Dichromatic Separation (SREDS) against other skin tone metrics and provide a use case for substituting race labels for SREDS scores in a privacy-preserving learning solution. Our findings suggest that SREDS consistently creates a skin tone metric with lower variability within each subject and SREDS values can be utilized as an alternative to the self-reported race labels at minimal drop in performance. Finally, we provide a publicly available and open-source implementation of SREDS to help the research community. Available at https://github.com/JosephDrahos/SRED

Computer Mediated Communication in Negotiations: The Effect of Intragroup Faultlines on Intergroup Communication and Outcomes

This work examines the effect of faultlines in virtual computer mediated communications of two collocated negotiation teams. We expand upon prior diversity literature by considering the effect of both surface and deep-level faultlines on the intergroup computer mediated communications in virtual negotiations. Faultlines are hypothetical lines that divide teams into multiple subgroups based on diversity attributes. We confirm that the effect of team diversity on intergroup computer mediated communications can be better captured through faultlines. Our results suggest that faultlines mediate the effect of diversity on teams’ computer mediated intergroup communication and that deep-level faultlines significantly lower the frequency and quality of intergroup communication of virtual negotiations

Deep Slap Fingerprint Segmentation for Juveniles and Adults

Many fingerprint recognition systems capture four fingerprints in one image. In such systems, the fingerprint processing pipeline must first segment each four-fingerprint slap into individual fingerprints. Note that most of the current fingerprint segmentation algorithms have been designed and evaluated using only adult fingerprint datasets. In this work, we have developed a human-annotated in-house dataset of 15790 slaps of which 9084 are adult samples and 6706 are samples drawn from children from ages 4 to 12. Subsequently, the dataset is used to evaluate the matching performance of the NFSEG, a slap fingerprint segmentation system developed by NIST, on slaps from adults and juvenile subjects. Our results reveal the lower performance of NFSEG on slaps from juvenile subjects. Finally, we utilized our novel dataset to develop the Mask-RCNN based Clarkson Fingerprint Segmentation (CFSEG). Our matching results using the Verifinger fingerprint matcher indicate that CFSEG outperforms NFSEG for both adults and juvenile slaps. The CFSEG model is publicly available at \url{https://github.com/keivanB/Clarkson_Finger_Segment

Face Liveness Detection Competition (LivDet-Face) - 2021

Liveness Detection (LivDet)-Face is an international competition series open to academia and industry. The competition’s objective is to assess and report state-of-the-art in liveness / Presentation Attack Detection (PAD) for face recognition. Impersonation and presentation of false samples to the sensors can be classified as presentation attacks and the ability for the sensors to detect such attempts is known as PAD. LivDet-Face 2021 * will be the first edition of the face liveness competition. This competition serves as an important benchmark in face presentation attack detection, offering (a) an independent assessment of the current state of the art in face PAD, and (b) a common evaluation protocol, availability of Presentation Attack Instruments (PAI) and live face image dataset through the Biometric Evaluation and Testing (BEAT) platform. The competition can be easily followed by researchers after it is closed, in a platform in which participants can compare their solutions against the LivDet-Face winners