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Likelihood-ratio based biometric score fusion is gaining much attention, since it maximizes accuracy if a log-likelihood ratio (LLR) is correctly estimated. It can also handle some missing query samples due to adverse physical conditions (e.g. injuries, illness) by setting the corresponding LLRs to 0. In this paper, we refer to the mode that allows missing query samples in such a way as a “modality...
Face recognition by machines has improved substantially in the past decade and now is at a level that compares favorably with humans for frontal faces acquired by digital single lens reflex cameras. We expand the comparison between humans and algorithms to still images and videos taken with digital point and shoot cameras. The data used for this comparison are from the Point and Shoot Face Recognition...
Iris recognition in the visible spectrum has many challenging aspects. Especially, for subjects with dark iris color, which is caused by higher melanin pigmentation and collagen fibrils, the pattern is not clearly observable under visible light. Thus, the verification performance is generally lowered due to limited texture visibility in the captured iris samples. In this work, we propose a novel method...
Biometric cryptosystem has been proven to be one of the promising approaches for template protection. Since most methods in this approach require binary input, to extend it for multiple modalities, binary template fusion is required. This paper addresses the issues of multi-biometrics' performance and security, and proposes a new binary template fusion method which could maximize the fused template...
Biometric recognition via eye movement-driven features is an emerging field of research. Eye movement cues are characterized by their non-static nature, the encapsulation of physical and behavioral traits, and the possibility to be recorded in tandem with other modalities, e.g. the iris. The BioEye 2015 competition was organized with the aim to boost the evolution of the eye movement biometrics field...
This work provides strong empirical evidence for a two-state generative model of typing behavior in which the user can be in either a passive or active state. Given key-press latencies with missing key names, the model is then used to spoof the key-press latencies of a user by exploiting the scaling behavior between inter-key distance and key-press latency. Key-press latencies with missing key names...
In this paper, we address the problem of iris recognition under less constrained environment. We propose a novel iris weight map for iris matching stage to improve the robustness of iris recognition to the noise and degradations in less constrained environment. The proposed iris weight map is class specific considering both the bit stability and bit discriminability of iris codes. It is the combination...
We present a face detection algorithm based on Deformable Part Models and deep pyramidal features. The proposed method called DP2MFD is able to detect faces of various sizes and poses in unconstrained conditions. It reduces the gap in training and testing of DPM on deep features by adding a normalization layer to the deep convolutional neural network (CNN). Extensive experiments on four publicly available...
Biometric menagerie is an important phenomenon in biometric systems, which focuses on distinguishing the minority of people who perform poorly and cause the majority of the errors (FAR and FRR). It can help to evaluate biometric systems and improve their performance by analyzing the animal like users. The fundamental step of this theory is the detection of animals. If the detection is not accurate,...
We present a method using facial attributes for continuous authentication of smartphone users. The binary attribute classifiers are trained using PubFig dataset and provide compact visual descriptions of faces. The learned classifiers are applied to the image of the current user of a mobile device to extract the attributes and then authentication is done by simply comparing the difference between...
In this paper, we focus on the problem of image set classification. Since existing methods utilize all available samples to model each image set, the corresponding time and storage requirements are high. Such methods are also susceptible to outliers. To address these challenges, we propose a method that jointly learns prototypes and a Mahalanobis distance. The prototypes learned represent the gallery...
Ballistic images of a cartridge case or bullet carry distinct “fingerprints” of the firearm, which is the foundation of widely used forensic examination in criminal investigations. In recent years, prior work has explored the effectiveness of correlation-based approaches in matching ballistic imagery. However, most of these studies focused on highly controlled situations and used relatively simple...
The use of near-IR images for face recognition has been proposed as a means to address illumination issues that can hinder standard visible light face matching. However, most existing non-experimental databases contain visible light images. This makes the matching of near-IR face images to visible light face images an interesting and useful challenge. Image pre-processing techniques can potentially...
Biometrics systems are being challenged at the sensor level using artefact presentation such as printed artefacts or electronic screen attacks. In this work, we propose a novel technique to detect the artefact iris images by decomposing the images into Laplacian pyramids of various scales and obtain frequency responses in different orientations. The obtained features are classified using a support...
We propose a new method called the Pokerface for extreme face illumination normalization. The Pokerface is a two-phase approach. It first aims at maximizing the minimum gap between adjacently-valued pixels while keeping the partial ordering of the pixels in the face image under extreme illumination condition, an intuitive effort based on order theory to unveil the underlying structure of a dark image...
The paper presents a reading-based eye movement biometrics model. The model is able to process passages of text and extract metrics that represent the physiological and behavioral aspects of the eye movements in reading. When tested on a database of eye movements from 103 individuals, the model yielded the Equal Error Rate of 10.2%. The proposed method performed better in the template-aging scenario...
Facial recognition at-a-distance in surveillance scenarios remains an open problem, particularly due to the small number of pixels representing the facial region. The use of pan-tilt-zoom (PTZ) cameras has been advocated to solve this problem, however, the existing approaches either rely on rough approximations or additional constraints to estimate the mapping between image coordinates and pan-tilt...
We present an algorithm for unconstrained face verification using Fisher vectors computed from frontalized off-frontal gallery and probe faces. In the training phase, we use the Labeled Faces in the Wild (LFW) dataset to learn the Fisher vector encoding and the joint Bayesian metric. Given an image containing the query face, we perform face detection and landmark localization followed by frontalization...
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