Biometric authentication during the COVID-19 and post-pandemic times require a touchless authentication mechanism. While existing studies showcase the use of fingerphoto for touchless authentication, a short video of the finger can provide many good-quality frames. This research presents the first publicly available finger-video dataset, titled Multi-Movement Finger-Video (MMFV) Database. The MMFV dataset has 3792 videos from 336 classes, acquired over two sessions, and spans three different movement types (pitch, yaw, and roll). To establish the baseline performance for the proposed MMFV database, we perform recognition using seven popular fingerprint and deep learning-based algorithms for fingerphoto recognition. The recognition is performed using a fixed, randomly selected frame from all the algorithms. Experimental results showcase that Siamese network-based verification provides the most optimal results across different movements, with observed EER as low as 2.70%.

MMFV: A Multi-Movement Finger-Video Database for Contactless Fingerprint Recognition

Face recognition from image sets acquired under unregulated and uncontrolled settings, such as at large distances, low resolutions, varying viewpoints, illumination, pose, and atmospheric conditions, is challenging. Face feature aggregation, which involves aggregating a set of N feature representations present in a template into a single global representation, plays a pivotal role in such recognition systems. Existing works in traditional face feature aggregation either utilize metadata or high-dimensional intermediate feature representations to estimate feature quality for aggregation. However, generating high-quality metadata or style information is not feasible for extremely low-resolution faces captured in long-range and high altitude settings. To overcome these limitations, we propose a feature distribution conditioning approach called CoNAN for template aggregation. Specifically, our method aims to learn a context vector conditioned over the distribution information of the incoming feature set, which is utilized to weigh the features based on their estimated informativeness. The proposed method produces state-of-the-art results on long-range unconstrained face recognition datasets such as BTS, and DroneSURF, validating the advantages of such an aggregation strategy.

CoNAN: Conditional Neural Aggregation Network For Unconstrained Face Feature Fusion

Cloth-changing person reidentification (ReID) is a newly emerging research topic that is aimed at addressing the issues of large feature variations due to cloth-changing and pedestrian view/pose changes. Although significant progress has been achieved by introducing extra information (e.g., human contour sketching information, human body keypoints, and 3D human information), cloth-changing person ReID is still challenging due to impressionable pedestrian representations. Moreover, human semantic information and pedestrian identity information are not fully explored. To solve these issues, we propose a novel identity-guided collaborative learning scheme (IGCL) for cloth-changing person ReID, where the human semantic is fully utilized and the identity is unchangeable to guide collaborative learning. First, we design a novel clothing attention degradation stream to reasonably reduce the interference caused by clothing information where clothing attention and mid-level collaborative learning are employed. Second, we propose a human semantic attention and body jigsaw stream to highlight the human semantic information and simulate different poses of the same identity. In this way, the extraction features not only focus on human semantic information that is unrelated to the background but also are suitable for pedestrian pose variations. Moreover, a pedestrian identity enhancement stream is further proposed to enhance the identity importance and extract more favorable identity robust features. Most importantly, all these streams are jointly explored in an end-to-end unified framework, and the identity is utilized to guide the optimization. Extensive experiments on five public clothing person ReID datasets demonstrate that the proposed IGCL significantly outperforms SOTA methods and that the extracted feature is more robust, discriminative, and clothing-irrelevant.

Identity-Guided Collaborative Learning for Cloth-Changing Person Reidentification

Contactless fingerprint matching using smartphone cameras can alleviate major challenges of traditional fingerprint systems including hygienic acquisition, portability and presentation attacks. However, development of practical and robust contactless fingerprint matching techniques is constrained by the limited availablity of large scale real-world datasets. To motivate further advances in contactless fingerprint matching across sensors, we introduce the RidgeBase benchmark dataset. RidgeBase consists of more than 15,000 contactless and contact-based fingerprint image pairs acquired from 88 individuals under different background and lighting conditions using two smartphone cameras and one flatbed contact sensor. Unlike existing datasets, RidgeBase is designed to promote research under different matching scenarios that include Single Finger Matching and Multi-Finger Matching for both contactless-to-contactless (CL2CL) and contact-to-contactless (C2CL) verification and identification. Furthermore, due to the high intra-sample variance in contactless fingerprints belonging to the same finger, we propose a set-based matching protocol inspired by the advances in facial recognition datasets. This protocol is specifically designed for pragmatic contactless fingerprint matching that can account for variances in focus, polarity and finger-angles. We report qualitative and quantitative baseline results for different protocols using a COTS fingerprint matcher (Verifinger) and a Deep CNN based approach on the RidgeBase dataset. The dataset can be downloaded here: https://www.buffalo.edu/cubs/research/datasets/ridgebase-benchmark-dataset.html

RidgeBase: A Cross-Sensor Multi-Finger Contactless Fingerprint Dataset

Person re-identification (re-ID) aims to retrieve images of the same identity from a gallery of person images across cameras and viewpoints. However, most works in person re-ID assume a short-term setting characterized by invariance in appearance. In contrast, a high visual variance can be frequently seen in a long-term setting due to changes in apparel and accessories, which makes the task more challenging. Therefore, learning identity-specific features agnostic of temporally variant features is crucial for robust long-term person Re-ID. To this end, we propose an Attribute De-biased Vision Transformer (AD-ViT) to provide direct supervision to learn identity-specific features. Specifically, we produce attribute labels for person instances and utilize them to guide our model to focus on identity features through gradient reversal. Our experiments on two long-term re-ID datasets - LTCC and NKUP show that the proposed work consistently outperforms current state-of-the-art methods.

Attribute De-biased Vision Transformer (AD-ViT) for Long-Term Person Re-identification

Cross-modal retrieval is a fundamental vision-language task with a broad range of practical applications. Text-to-image matching is the most common form of cross-modal retrieval where, given a large database of images and a textual query, the task is to retrieve the most relevant set of images. Existing methods utilize dual encoders with an attention mechanism and a ranking loss for learning embeddings that can be used for retrieval based on cosine similarity. Despite the fact that these methods attempt to perform semantic alignment across visual regions and textual words using tailored attention mechanisms, there is no explicit supervision from the training objective to enforce such alignment. To address this, we propose NAPReg, a novel regularization formulation that projects high-level semantic entities i.e Nouns into the embedding space as shared learnable proxies. We show that using such a formulation allows the attention mechanism to learn better word-region alignment while also utilizing region information from other samples to build a more generalized latent representation for semantic concepts. Experiments on three benchmark datasets i.e. MS-COCO, Flickr30k and Flickr8k demonstrate that our method achieves state-of-the-art results in cross-modal metric learning for text-image and image-text retrieval tasks. Code: https://github.com/bhavinjawade/NAPReq

NAPReg: Nouns As Proxies Regularization for Semantically Aware Cross-Modal Embeddings

Learning to localize the sound source in videos without explicit annotations is a novel area of audio-visual research. Existing work in this area focuses on creating attention maps to capture the correlation between the two modalities to localize the source of the sound. In a video, oftentimes, the objects exhibiting movement are the ones generating the sound. In this work, we capture this characteristic by modeling the optical flow in a video as a prior to better aid in localizing the sound source. We further demonstrate that the addition of flow-based attention substantially improves visual sound source localization. Finally, we benchmark our method on standard sound source localization datasets and achieve state-of-the-art performance on the Soundnet Flickr and VGG Sound Source datasets. Code: https://github.com/denfed/heartheflow.

Bhavin Jawade

Papers

RidgeBase: A Cross-Sensor Multi-Finger Contactless Fingerprint Dataset

Attribute De-biased Vision Transformer (AD-ViT) for Long-Term Person Re-identification

NAPReg: Nouns As Proxies Regularization for Semantically Aware Cross-Modal Embeddings

CoNAN: Conditional Neural Aggregation Network For Unconstrained Face Feature Fusion

Hear The Flow: Optical Flow-Based Self-Supervised Visual Sound Source Localization