Biometric Authentication with a Timeless Learner (BATL)


Biometric systems are vulnerable to well-crafted presentation attacks. Sponsored by IARPA’s Odin/Thor program, BATL develops systems and algorithms resilient to presentation attacks (spoofing) of biometric authentication systems, including face, iris and fingerprint systems. Our approach follows a hybrid solution, based on novel multispectral multi-camera systems for data capture along with powerful machine learning models for data analysis and inference.


Collaborating Institutions


December, 2021

IEEE International Conference on Automatic Face and Gesture Recognition (FG)

A large number of deep neural network-based techniques have been developed to address the challenging problem of face presentation attack detection (PAD). Whereas such techniques’ focus has been on improving PAD performance in terms of classification accuracy and robustness against unseen attacks and environmental conditions, there exists little attention on the explainability of PAD predictions. In this paper, we tackle the problem of explaining PAD predictions through natural language.

October, 2021

IEEE/CVF International Conference on Computer Vision (ICCV)

State of-the-art face antispoofing systems are vulnerable to novel types of attacks that are never seen during training. Moreover, even if such attacks are correctly detected, these systems lack the ability to adapt to newly encountered attacks. In this paper, we enable a deep neural network to detect anomalies in the observed input data points as potential new types of attacks by suppressing the confidence-level of the network outside the training samples’ distribution. We then use experience replay to update the model to incorporate knowledge about new types of attacks without forgetting the past learned attack types.

July, 2021

IEEE Transactions on Biometrics, Behavior, and Identity Science (TBIOM)

Fingerprint presentation attack detection is becoming an increasingly challenging problem due to the continuous advancement of attack preparation techniques, which generate realistic-looking fake fingerprint presentations. In this work, rather than relying on legacy fingerprint images, which are widely used in the community, we study the usefulness of multiple recently introduced sensing modalities. Our study covers front-illumination imaging using short-wave-infrared, near-infrared, and laser illumination; and back-illumination imaging using near-infrared light.

April, 2021

IEEE Sensors Journal (JSEN)

In this work, we present a general framework for building a biometrics system capable of capturing multispectral data from a series of sensors synchronized with active illumination sources. The framework unifies the system design for different biometric modalities and its realization on face, finger and iris data is described in detail. 


Intelligence Advanced Research Projects Activity