Biometrics based privacy-preserving authentication and mobile template protection

Smart mobile devices are playing a more and more important role in our daily life. Cancelable biometrics is a promising mechanism to provide authentication to mobile devices and protect biometric templates by applying a noninvertible transformation to raw biometric data. However, the negative effect of nonlinear distortion will usually degrade the matching performance significantly, which is a nontrivial factor when designing a cancelable template. Moreover, the attacks via record multiplicity (ARM) present a threat to the existing cancelable biometrics, which is still a challenging open issue. To address these problems, in this paper, we propose a new cancelable fingerprint template which can not only mitigate the negative effect of nonlinear distortion by combining multiple feature sets, but also defeat the ARM attack through a proposed feature decorrelation algorithm. Our work is a new contribution to the design of cancelable biometrics with a concrete method against the ARM attack. Experimental results on public databases and security analysis show the validity of the proposed cancelable template

Automated Algorithm for the Identification of Artifacts in Mottled and Noisy Images

We describe a method for automatically classifying image-quality defects on printed documents. The proposed approach accepts a scanned image where the defect has been localized a priori and performs several appropriate image processing steps to reveal the region of interest. A mask is then created from the exposed region to identify bright outliers. Morphological reconstruction techniques are then applied to emphasize relevant local attributes. The classification of the defects is accomplished via a customized tree classifier that utilizes size or shape attributes at corresponding nodes to yield appropriate binary decisions. Applications of this process include automated/assisted diagnosis and repair of printers/copiers in the field in a timely fashion. The proposed technique was tested on a database of 276 images of synthetic and real-life defects with 94.95% accuracy

Language Prompt for Autonomous Driving

A new trend in the computer vision community is to capture objects of interest following flexible human command represented by a natural language prompt. However, the progress of using language prompts in driving scenarios is stuck in a bottleneck due to the scarcity of paired prompt-instance data. To address this challenge, we propose the first object-centric language prompt set for driving scenes within 3D, multi-view, and multi-frame space, named NuPrompt. It expands Nuscenes dataset by constructing a total of 35,367 language descriptions, each referring to an average of 5.3 object tracks. Based on the object-text pairs from the new benchmark, we formulate a new prompt-based driving task, \ie, employing a language prompt to predict the described object trajectory across views and frames. Furthermore, we provide a simple end-to-end baseline model based on Transformer, named PromptTrack. Experiments show that our PromptTrack achieves impressive performance on NuPrompt. We hope this work can provide more new insights for the autonomous driving community. Dataset and Code will be made public at \href{https://github.com/wudongming97/Prompt4Driving}{https://github.com/wudongming97/Prompt4Driving}

Referring Multi-Object Tracking

Existing referring understanding tasks tend to involve the detection of a single text-referred object. In this paper, we propose a new and general referring understanding task, termed referring multi-object tracking (RMOT). Its core idea is to employ a language expression as a semantic cue to guide the prediction of multi-object tracking. To the best of our knowledge, it is the first work to achieve an arbitrary number of referent object predictions in videos. To push forward RMOT, we construct one benchmark with scalable expressions based on KITTI, named Refer-KITTI. Specifically, it provides 18 videos with 818 expressions, and each expression in a video is annotated with an average of 10.7 objects. Further, we develop a transformer-based architecture TransRMOT to tackle the new task in an online manner, which achieves impressive detection performance and outperforms other counterparts. The dataset and code will be available at https://github.com/wudongming97/RMOT.Comment: Accpeted by CVPR 2023. The dataset and code will be available at https://github.com/wudongming97/RMO