Quantifying Biometric Characteristics of Hand Gestures Through Feature Space Probing and Identity-Level Cross-Gesture Disentanglement

We present the delta-gesture biometrics quantification assessment (DGBQA) framework which estimates the biometric characteristics of hand gestures. The proposed framework is aimed at learning generic motion-representations of gestures instead of subject-specific details from a large number of identities. It also enables the biometric scores to be estimated for a set of gestures at a time instead of having to estimate these one at a time. In the first step, it formulates a feature space which is identity and gesture aware, and in the second step, it proceeds to compute biometric scores using inter-subject and intra-subject distance measures in the feature space. However, due to the inclusion of identity-aware objective, the identity details tend to be shared across gestures. We refer to this as identity sharing and this can lead to the score for different gestures being dependent on each other. To address this issue, we introduce an identity-level cross-gesture disentanglement loss
$(\mathscr{L}_{ICGD})$
which encourages the different gestures belonging to the same identity to be orthogonal in the feature space. We demonstrate the efficacy of the proposed biometric quantification framework and the disentanglement loss function through extensive experiments on four datasets and using standard as well as proposed novel evaluation metrics. Our analysis indicates that gestures involving multiple coarse movements are better for biometrics.