The vulnerability of face recognition systems towards evolving presentation attacks has drawn significant interest in the last decade. In this paper, we present an empirical study on both vulnerability analysis and presentation attack detection for commercial face recognition systems (FRS) using custom 3D silicone face masks corresponding to real subjects. To this end, a new database is collected consisting of 8 custom 3D silicone masks together with bona fide presentations of the corresponding subjects using three different devices (smartphones). The vulnerability of FRS for 3D face silicone face masks is effectively evaluated using two well-known commercial-off-the-shelf (COTS) FRS (Verilook from Neurotechnology, and the Cognitec Face-VACS). Further, extensive experiments are carried out to evaluate the effectiveness of five state-of-the-art presentation attack detection (PAD) techniques for detecting such masks. Key insights on silicone mask PAD are provided along with a discussion on the accuracy achieved in our experiments.

This work focuses on detecting presentation attacks (PA) mounted using custom silicone masks. Face recognition (FR) systems have been shown to be highly vulnerable to PAs based on such masks bhattacharjee:2018, raghavendra:2019. Here we explore the use of multispectral data (color imagery, near infrared (NIR) imagery and thermal imagery) for face presentation attack detection (PAD), specifically against the custom silicone mask attacks. Using a new dataset (XCSMAD) representing 21 custom made masks, we establish the baseline performance of several commonly used face-PAD methods, on the different imaging channels. Considering thermal imagery in particular, our experiments show that low-cost thermal imaging devices are as effective in face-PAD as more expensive thermal cameras, for mask-based attacks. This result reinforces the case for the use of thermal data in face-PAD. We also demonstrate that fusing information from multiple channels leads to significant improvement in face-PAD performance. Finally, we propose a new approach to face-PAD of custom silicone masks using a convolutional neural network (CNN). On individual spectral channels, the proposed approach achieves state-of-the-art results. Using multispectral-fusion, the proposed CNN-based method significantly outperforms the baseline methods. The new dataset and source-code for our experiments is freely available for research purposes.

For the automotive industry moving towards personalized applications and experiences, the identification of the person inside vehicle is necessary; and it must be carried out in a secure manner. In this paper, we propose a unique face presentation attack detection (PAD) system for operation inside a passenger vehicle. A typical in-vehicular face PAD system is required to function with several constraints such as bounded sensing (imaging) capabilities, limited computing resources on embedded devices, real-time inference, and essentially, very high accuracy. In this work, we develop a face PAD system for automotive domain, relying on a single NIR camera, to continually verify whether the driver’s face is bona-fide or not. Our work has two main contributions: first, a lightweight face PAD framework has been developed using a 9-layer convolutional neural network (CNN). With its compact size and limited set of operators, it can be deployed in a resource constrained embedded device to achieve a near real-time inference. To alleviate the problem of limited training data (face PAD in NIR) for a given system, we develop an efficient mechanism to obtain this CNN through the combination of adaptation of domain-specific layers and task-specific fine-tuning of a base CNN. As the second contribution, we collect a large face PAD dataset with 5800+ videos, acquired in NIR (940 nm ) illumination, for in-vehicular use-cases. This dataset, named VFPAD, captures several real-world variations in terms of environmental settings, illumination, subject’s pose, and appearances. Based on the VFPAD dataset, we demonstrate that the proposed face PAD method achieves very high performance (overall accuracy $\approx 98.0$ %), and also outperforms several baseline face PAD methods. The dataset will be shared with the wider scientific community for research purposes.

Full-view finger vein (FV) biometrics systems capture multiple FV images of the presented finger ensuring that the entire surface of the finger is covered. Existing full-view FV systems suffer from three common problems: large device size, high cost for multi-camera system, and sub-optimal illumination in the recorded FV images. To address the problem of device size, we propose a novel Mirror-based Full-view FV (MFFV) capture device. The MFFV device has a compact size by using mirror-reflection approach. We reduce the cost of the device by using low-cost components, in particular, consumer-grade cameras. To address the problems of lower-quality images captured by such cameras and obtain optimally illuminated FV images, we propose a two-step approach. The first step is a Multi-illumination Intensities FV (MIFV) capture strategy, which capture the FV image set with varying illumination intensities. In the second step, a FV illumination adaptation (FVIA) algorithm is proposed to select the optimally illuminated FV image from the MIFV image set. Using the proposed MFFV device, we collect a comprehensive dataset, namely MFFV dataset, along with reproducible baseline FV authentication results for both single-view and full-view FV. Our experimental results demonstrate that the MIFV capture strategy as well as the FVIA algorithm can effectively improve the authentication performance, and that the full-view FV authentication is significantly superior than the single-view FV authentication. The source-code and dataset for reproducing our experimental results are publicly available ¹ .

Current finger-vein or palm-vein recognition systems usually require direct contact of the subject with the apparatus. This can be problematic in environments where hygiene is of primary importance. In this work we present a contactless vascular biometrics sensor platform named sweet which can be used for hand vascular biometrics studies (wrist, palm, and finger-vein) and surface features such as palmprint. It supports several acquisition modalities such as multi-spectral Near-Infrared (NIR), RGB-color, Stereo Vision (SV) and Photometric Stereo (PS). Using this platform we collected a dataset consisting of the fingers, palm and wrist vascular data of 120 subjects. We present biometric experimental results, focusing on Finger-Vein Recognition (FVR). Finally, we discuss fusion of multiple modalities. The acquisition software, parts of the hardware design, the new FV dataset, as well as source-code for our experiments are publicly available for research purposes.