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Face sketch to digital image matching is an important challenge of face recognition that involves matching across different domains. Current research efforts have primarily focused on extracting domain invariant representations or learning a mapping from one domain to the other. In this research, we propose a novel transform learning based approach termed as DeepTransformer, which learns a transformation...
Matching facial sketches to digital face images has widespread application in law enforcement scenarios. Recent advancements in technology have led to the availability of sketch generation tools, minimizing the requirement of a sketch artist. While these sketches have helped in manual authentication, matching composite sketches with digital mugshot photos automatically show high modality gap. This...
Face recognition systems are susceptible to presentation attacks such as printed photo attacks, replay attacks, and 3D mask attacks. These attacks, primarily studied in visible spectrum, aim to obfuscate or impersonate a person's identity. This paper presents a unique multispectral video face database for face presentation attack using latex and paper masks. The proposed Multispectral Latex Mask based...
The growth of online services has resulted in a great need for tools to secure systems from would-be attackers without compromising the user experience. CAPTCHAs (Completely Automated Public Turing Tests to Tell Computers and Humans Apart) are one tool for this purpose, but their popular text-based form has been rendered insecure by improvements in character recognition technology. In this paper,...
Classification is an important pattern recognition paradigm with a multitude of applications in popular research problems. Utilizing multiple data representations to improve the accuracy of classification has been explored in literature. However, approaches such as combining classifiers using majority voting and score level fusion do not utilize the underlying structure of the data which is available...
Gender is one of the most common attributes used to describe an individual. It is used in multiple domains such as human computer interaction, marketing, security, and demographic reports. Research has been performed to automate the task of gender recognition in constrained environment using face images, however, limited attention has been given to gender classification in unconstrained scenarios...
This paper focuses on decoding the process of face verification in the human brain using fMRI responses. 2400 fMRI responses are collected from different participants while they perform face verification on genuine and imposter stimuli face pairs. The first part of the paper analyzes the responses covering both cognitive and fMRI neuro-imaging results. With an average verification accuracy of 64.79%...
Classifier fusion is a well-studied problem in which decisions from multiple classifiers are combined at the score, rank, or decision level to obtain better results than a single classifier. Subsequently, various techniques for combining classifiers at each of these levels have been proposed in the literature. Many popular methods entail scaling and normalizing the scores obtained by each classifier...
Biometric systems can be attacked in several ways and the most common being spoofing the input sensor. Therefore, anti-spoofing is one of the most essential prerequisite against attacks on biometric systems. For face recognition it is even more vulnerable as the image capture is non-contact based. Several anti-spoofing methods have been proposed in the literature for both contact and non-contact based...
Large scale biometrics projects rely on capturing images/signal from multiple sensors. For example, in India's Aadhaar project, multiple fingerprint sensors of different make and model are used for data collection. Similarly, in law enforcement applications, different agencies use different fingerprint sensors. These scenarios cause two potential problems: (i) sensor inter-operability and (ii) protecting/recording...
Low cost RGB-D cameras are gaining significant popularity in surveillance scenarios. While RGB images contain good quality discriminative information, depth images captured in uncontrolled environment at a distance does not provide accurate depth map. In this research, we present a learning based reconstruction and mapping algorithm to generate a feature rich representation from the RGB images. These...
Human iris is considered a reliable and accurate modality for biometric recognition due to its unique texture information. However, similar to other biometric modalities, iris recognition systems are also vulnerable to presentation attacks (commonly called spoofing) that attempt to conceal or impersonate identity. Examples of typical iris spoofing attacks are printed iris images, textured contact...
Advancing state of the art in face recognition and bridging the gap between laboratory and real-world scenarios require the availability of challenging databases. One of the challenging applications of face recognition is surveillance, where unconstrained video data is captured both in day and night time (visible and near infrared spectrum). These videos have multiple subjects in each frame, which...
Face spoofing can be performed in a variety of ways such as replay attack, print attack, and mask attack to deceive an automated recognition algorithm. To mitigate the effect of spoofing attempts, face anti-spoofing approaches aim to distinguish between genuine samples and spoofed samples. The focus of this paper is to detect spoofing attempts via Haralick texture features. The proposed algorithm...
CAPTCHA (Completely Automated Public Turing Test to tell Computers and Humans Apart) have been a common tool for preventing unauthorized access to websites for over a decade, but increasingly sophisticated optical character recognition algorithms and attack strategies have rendered traditional CAPTCHAs insecure. In this paper, we propose a new CAPTCHA incorporating multiple biometric modalities. Users...
Ocular recognition algorithms, including iris matching, have been used in several applications including large scale national ID projects such as India's Aadaar. Deployment of large-scale biometric systems is expected to rely on using multiple devices including mobile devices to ensure widespread adoption of biometric recognition systems. Ocular images captured using mobile devices may have challenges...
Face identification from low quality and low resolution Near-Infrared (NIR) face images is a challenging problem. Since surveillance cameras typically acquire images at a large standoff distance, the effective resolution of the face is not large enough to identify the individuals. Moreover for a 24-hour surveillance footage, images in low light and at nighttime are acquired in NIR mode which makes...
Person recognition is a challenging research problem particularly if the images are captured at a distance and only ocular region is present. In this research, we present a framework that extracts multiple features from iris and periocular regions from near infrared images captured at a distance of 2 meters or more. Using these features and random decision forest, fusion and classification is performed...
Face recognition under uncontrolled environment persists to be an unresolved problem having challenges such as varying pose, illumination, occlusion etc. In this research, we propose an algorithm for identification of faces with pose and illumination variations. An adaptive dictionary learning framework built upon group sparse representation classifier is presented in order to learn dictionary parameters...
An Ant Colony Optimization (ACO) based Fractional Fuzzy PID controller is proposed in this paper. The resulting controller Ant Colony Fractional Fuzzy PID (AFrFPID) Controller incorporates the characteristics of the Ant Colony System and Fuzzy Control for controlling integer and fractional order plants. Fractional Order PID (FOPID) controllers show better performance for systems that have non-linear...
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