Learning Domain Invariant Information to Enhance Presentation Attack Detection in Visible Face Recognition Systems

Face signatures, including size, shape, texture, skin tone, eye color, appearance, and scars/marks, are widely used as discriminative, biometric information for access control. Despite recent advancements in facial recognition systems, presentation attacks on facial recognition systems have become increasingly sophisticated. The ability to detect presentation attacks or spoofing attempts is a pressing concern for the integrity, security, and trust of facial recognition systems. Multi-spectral imaging has been previously introduced as a way to improve presentation attack detection by utilizing sensors that are sensitive to different regions of the electromagnetic spectrum (e.g., visible, near infrared, long-wave infrared). Although multi-spectral presentation attack detection systems may be discriminative, the need for additional sensors and computational resources substantially increases complexity and costs. Instead, we propose a method that exploits information from infrared imagery during training to increase the discriminability of visible-based presentation attack detection systems. We introduce (1) a new cross-domain presentation attack detection framework that increases the separability of bonafide and presentation attacks using only visible spectrum imagery, (2) an inverse domain regularization technique for added training stability when optimizing our cross-domain presentation attack detection framework, and (3) a dense domain adaptation subnetwork to transform representations between visible and non-visible domains. Adviser: Benjamin Rigga

Infrared face recognition: a comprehensive review of methodologies and databases

Automatic face recognition is an area with immense practical potential which includes a wide range of commercial and law enforcement applications. Hence it is unsurprising that it continues to be one of the most active research areas of computer vision. Even after over three decades of intense research, the state-of-the-art in face recognition continues to improve, benefitting from advances in a range of different research fields such as image processing, pattern recognition, computer graphics, and physiology. Systems based on visible spectrum images, the most researched face recognition modality, have reached a significant level of maturity with some practical success. However, they continue to face challenges in the presence of illumination, pose and expression changes, as well as facial disguises, all of which can significantly decrease recognition accuracy. Amongst various approaches which have been proposed in an attempt to overcome these limitations, the use of infrared (IR) imaging has emerged as a particularly promising research direction. This paper presents a comprehensive and timely review of the literature on this subject. Our key contributions are: (i) a summary of the inherent properties of infrared imaging which makes this modality promising in the context of face recognition, (ii) a systematic review of the most influential approaches, with a focus on emerging common trends as well as key differences between alternative methodologies, (iii) a description of the main databases of infrared facial images available to the researcher, and lastly (iv) a discussion of the most promising avenues for future research.Comment: Pattern Recognition, 2014. arXiv admin note: substantial text overlap with arXiv:1306.160

An Extensive Review on Spectral Imaging in Biometric Systems: Challenges and Advancements

Spectral imaging has recently gained traction for face recognition in biometric systems. We investigate the merits of spectral imaging for face recognition and the current challenges that hamper the widespread deployment of spectral sensors for face recognition. The reliability of conventional face recognition systems operating in the visible range is compromised by illumination changes, pose variations and spoof attacks. Recent works have reaped the benefits of spectral imaging to counter these limitations in surveillance activities (defence, airport security checks, etc.). However, the implementation of this technology for biometrics, is still in its infancy due to multiple reasons. We present an overview of the existing work in the domain of spectral imaging for face recognition, different types of modalities and their assessment, availability of public databases for sake of reproducible research as well as evaluation of algorithms, and recent advancements in the field, such as, the use of deep learning-based methods for recognizing faces from spectral images

Towards a Robust Thermal-Visible Heterogeneous Face Recognition Approach Based on a Cycle Generative Adversarial Network

Security is a sensitive area that concerns all authorities around the world due to the emerging terrorism phenomenon. Contactless biometric technologies such as face recognition have grown in interest for their capacity to identify probe subjects without any human interaction. Since traditional face recognition systems use visible spectrum sensors, their performances decrease rapidly when some visible imaging phenomena occur, mainly illumination changes. Unlike the visible spectrum, Infrared spectra are invariant to light changes, which makes them an alternative solution for face recognition. However, in infrared, the textural information is lost. We aim, in this paper, to benefit from visible and thermal spectra by proposing a new heterogeneous face recognition approach. This approach includes four scientific contributions. The first one is the annotation of a thermal face database, which has been shared via Github with all the scientific community. The second is the proposition of a multi-sensors face detector model based on the last YOLO v3 architecture, able to detect simultaneously faces captured in visible and thermal images. The third contribution takes up the challenge of modality gap reduction between visible and thermal spectra, by applying a new structure of CycleGAN, called TV-CycleGAN, which aims to synthesize visible-like face images from thermal face images. This new thermal-visible synthesis method includes all extreme poses and facial expressions in color space. To show the efficacy and the robustness of the proposed TV-CycleGAN, experiments have been applied on three challenging benchmark databases, including different real-world scenarios: TUFTS and its aligned version, NVIE and PUJ. The qualitative evaluation shows that our method generates more realistic faces. The quantitative one demonstrates that the proposed TV -CycleGAN gives the best improvement on face recognition rates. Therefore, instead of applying a direct matching from thermal to visible images which allows a recognition rate of 47,06% for TUFTS Database, a proposed TV-CycleGAN ensures accuracy of 57,56% for the same database. It contributes to a rate enhancement of 29,16%, and 15,71% for NVIE and PUJ databases, respectively. It reaches an accuracy enhancement of 18,5% for the aligned TUFTS database. It also outperforms some recent state of the art methods in terms of F1-Score, AUC/EER and other evaluation metrics. Furthermore, it should be mentioned that the obtained visible synthesized face images using TV-CycleGAN method are very promising for thermal facial landmark detection as a fourth contribution of this paper