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1. Unconstrained visible spectrum iris with textured contact lens variations: Database and benchmarking

   https://doi.org/10.1109/BTAS.2017.8272744  

   Yadav, Daksha; Kohli, Naman; Vatsa, Mayank; Singh, Richa; Noore, Afzel (October 2017, International Joint Conference on Biometrics (IJCB))

   Iris recognition in visible spectrum has developed into an active area of research. This has elevated the importance of efficient presentation attack detection algorithms, particularly in security based critical applications. In this paper, we present the first detailed analysis of the effect of contact lenses on iris recognition in visible spectrum. We introduce the first contact lens database in visible spectrum, Unconstrained Visible Contact Lens Iris (UVCLI) Database, containing samples from 70 classes with subjects wearing textured contact lenses in indoor and outdoor environments across multiple sessions. We observe that textured contact lenses degrade the visible spectrum iris recognition performance by over 25% and thus, may be utilized intentionally or unintentionally to attack existing iris recognition systems. Next, three iris presentation attack detection (PAD) algorithms are evaluated on the proposed database and highest PAD accuracy of 82.85% is observed. This illustrates that there is a significant scope of improvement in developing efficient PAD algorithms for detection of textured contact lenses in unconstrained visible spectrum iris images. 

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2. SWAPPED! Digital face presentation attack detection via weighted local magnitude pattern

   https://doi.org/10.1109/BTAS.2017.8272754  

   Agarwal, Akshay; Singh, Richa; Vatsa, Mayank; Noore, Afzel (October 2017, International Joint Conference on Biometrics (IJCB))

   Advancements in smartphone applications have empowered even non-technical users to perform sophisticated operations such as morphing in faces as few tap operations. While such enablements have positive effects, as a negative side, now anyone can digitally attack face (biometric) recognition systems. For example, face swapping application of Snapchat can easily create “swapped” identities and circumvent face recognition system. This research presents a novel database, termed as SWAPPED - Digital Attack Video Face Database, prepared using Snapchat’s application which swaps/stitches two faces and creates videos. The database contains bonafide face videos and face swapped videos of multiple subjects. Baseline face recognition experiments using commercial system shows over 90% rank-1 accuracy when attack videos are used as probe. As a second contribution, this research also presents a novel Weighted Local Magnitude Pattern feature descriptor based presentation attack detection algorithm which outperforms several existing approaches. 

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3. Iris Presentation Attack via Textured Contact Lens in Unconstrained Environment

   https://doi.org/10.1109/WACV.2018.00061  

   Yadav, Daksha; Kohli, Naman; Yadav, Shivangi; Vatsa, Mayank; Singh, Richa; Noore, Afzel (March 2018, IEEE Winter Conference on Applications of Computer Vision (WACV))

   The widespread use of smartphones has spurred the research in mobile iris devices. Due to their convenience, these mobile devices are also utilized in unconstrained outdoor scenarios. This has necessitated the development of reliable iris recognition algorithms for such uncontrolled environment. At the same time, iris presentation attacks pose a major challenge to current iris recognition systems. It has been shown that print attacks and textured contact lens may significantly degrade the iris recognition performance. Motivated by these factors, we present a novel Mobile Uncontrolled Iris Presentation Attack Database (MUIPAD). The database contains more than 10,000 iris images that are acquired with and without textured contact lenses in indoor and outdoor environments using a mobile sensor. We also investigate the efficacy of textured contact lens in identity impersonation and obfuscation. Moreover, we demonstrate the effectiveness of deep learning based features for iris presentation attack detection on the proposed database. 

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4. Face description using anisotropic gradient: thermal infrared to visible face recognition

   https://doi.org/10.1117/12.2304898  

   Wan, Qianwen; Rao, Shishir Paramathma; Panetta, Karen; Agaian, Sos S.; Kaszowska, Aleksandra; Taylor, Holly; Voronina, V. (May 2018, Mobile Multimedia/Image Processing, Security, and Applications 2018)

   Agaian, Sos S.; Jassim, Sabah A. (Ed.)

   Face recognition technologies have been in high demand in the past few decades due to the increase in human-computer interactions. It is also one of the essential components in interpreting human emotions, intentions, facial expressions for smart environments. This non-intrusive biometric authentication system relies on identifying unique facial features and pairing alike structures for identification and recognition. Application areas of facial recognition systems include homeland and border security, identification for law enforcement, access control to secure networks, authentication for online banking and video surveillance. While it is easy for humans to recognize faces under varying illumination conditions, it is still a challenging task in computer vision. Non-uniform illumination and uncontrolled operating environments can impair the performance of visual-spectrum based recognition systems. To address these difficulties, a novel Anisotropic Gradient Facial Recognition (AGFR) system that is capable of autonomous thermal infrared to visible face recognition is proposed. The main contribution of this paper includes a framework for thermal/fused-thermal-visible to visible face recognition system and a novel human-visual-system inspired thermal-visible image fusion technique. Extensive computer simulations using CARL, IRIS, AT&T, Yale and Yale-B databases demonstrate the efficiency, accuracy, and robustness of the AGFR system. Keywords: Infrared thermal to visible facial recognition, anisotropic gradient, visible-to-visible face recognition, nonuniform illumination face recognition, thermal and visible face fusion method 

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5. Deep Cross Polarimetric Thermal-to- visible Face Recognition

   Iranmanesh, Seyed-Mehdi; Nasrabadi, Nasser M (February 2018, The 11th IAPR International Conference on Biometrics (ICB 2018))

   In this paper, we present a deep coupled learning framework to address the problem of matching polarimetric thermal face photos against a gallery of visible faces. Polarization state information of thermal faces provides the missing textural and geometrics details in the thermal face imagery which exist in visible spectrum. we propose a coupled deep neural network architecture which leverages relatively large visible and thermal datasets to overcome the problem of overfitting and eventually we train it by a polarimetric thermal face dataset which is the first of its kind. The proposed architecture is able to make full use of the polarimetric thermal information to train a deep model compared to the conventional shallow thermal-to-visible face recognition methods. Proposed coupled deep neural network also finds global discriminative features in a nonlinear embedding space to relate the polarimetric thermal faces to their corresponding visible faces. The results show the superiority of our method compared to the state-of-the-art models in cross thermal-to-visible face recognition algorithms. 

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