This paper explores the application of millimeter-wave (mmWave) radar in biometric recognition. As a non-visual human sensing technology, mmWave radar captures reflection properties and micro-movements, providing a complementary modality to visual appearance. We implemented a complete system pipeline to utilize mmWave sensing for individual recognition. Based on physiological mechanisms, we design preprocessing methods to extract intuitive biometric feature maps, concerning body reflections, cardiopulmonary activity, and micro-motion frequencies. To address the data uncertainty, a dynamic pole-based learning strategy is proposed to construct compact and discriminated feature distributions. In real-world evaluations, the system achieves 95.12% accuracy and an Equal Error Rate (EER) of 1.96%. This work leverages the advantages of mmWave radar for flexible, unconstrained, and private biometric systems. From a non-visual sensing perspective, it explores novel modalities as unique biometric cues, demonstrating significant value of research and applications.

Human Action Recognition (HAR) is pivotal in advancing applications from surveillance to healthcare, but predominantly focuses on easily observable, macro-level actions such as running or jumping. Micro-Action Recognition (MAR), however, delves into the subtle, often involuntary motions like postural shifts, brief gestures, or faint facial twitches, which are critical for revealing underlying emotional states, intentions, or stress levels. MAR presents unique challenges due to the ephemeral nature of micro-actions, their fine-grained inter-class similarities, and significant class imbalance. To overcome these obstacles, our approach draws inspiration from the hierarchical and context-sensitive capabilities of the human visual system. We propose a biologically motivated, multi-pathway framework that cohesively integrates global context analysis, rapid temporal scanning, and meticulous fine-grained scrutiny. This framework combines skeletal dynamics, subtle motion amplitude cues, and RGB-based contextual features to enable a comprehensive and robust recognition of micro-actions, even in unconstrained environments. Our experimental results on the MA-52 dataset demonstrate leading performance, significantly advancing MAR research and broadening the spectrum of applications that require a nuanced understanding of human behavior.

High throughput is demanding in real-world iris recognition applications. The challenges mainly originate from the variability in image quality under high-throughput capture conditions. Most of the degraded images are typically filtered out by traditional iris systems through Image Quality Assessment (IQA) module, adversely affecting efficiency and leading to low throughput and poor user experience. Therefore, a better and practical solution is to make the utmost of degraded iris images. In order to investigate the key problems of high-throughput iris recognition, we collect a novel iris sequence dataset under Near-infrared (NIR) illumination. This dataset is specifically constructed for high-throughput evaluation, which faithfully simulates the process of iris sequence acquisition in real-world iris systems. Comprehensive evaluations were conducted to figure out the deficiencies of current iris recognition algorithms. To this end, a testing methodology along with specific evaluation metrics is proposed. It is capable of assessing the throughput performance, e.g., the newly proposed Frame Consumption per Match (FCM). Through performance analysis, several insights were gathered to guide potential directions for developing high-throughput iris recognition algorithms. Furthermore, we consider to leverage iris sequence features for better throughput performance. Continuity sequence criteria and cumulative sequence feature strategy are proposed to enhance the throughput performance of existing algorithms with minimal cost. In summary, this work provides valuable data and rational insights for high-throughput iris recognition studies. The datasets and evaluation toolkit are publicly available on our website1.

Light Field Focal Stack (LFFS) can be efficiently rendered from a light field (LF) image captured by plenoptic cameras. Differences in the 3D surface and texture of biometric samples are internally reflected in the defocus blur and local patterns between the rendered slices of LFFS. This unique property makes LFFS quite appropriate to differentiate presentation attack instruments (PAIs) from bona fide samples. A patch-based dual-view network (PDVN) is proposed in this paper to leverage the merits of LFFS for face presentation attack detection (PAD). First, original LFFS data are divided into various local patches along spatial dimensions, which distracts the model from learning the useless facial semantics and greatly relieve the problem of insufficient samples. The strategy of dual-view branches is innovatively proposed, wherein the original view and microscopic view can simultaneously contribute to liveness detection. Separable 3D convolution on the focal dimension is verified to be more effective than vanilla 3D convolution for extracting discriminative features from LFFS data. The voting mechanism on predictions of patch LFFS samples further strengthens the robustness of the proposed framework. PDVN is compared with other face PAD methods on IST LLFFSD dataset and achieves perfect performance, i.e., ACER drops to 0.