Fingerphoto Authentication Using Smartphone Camera Captured Under Varying Environmental Conditions
01 Jan 2017-pp 119-144
TL;DR: A novel end-to-end finger photo matching pipeline is proposed by studying the effect of different environmental conditions in fingerphoto matching by creating a publicly available fingerphoto dataset, IIITD SmartPhone Fingerphoto Database v1, and shows that the proposed matching pipeline provides an improved performance when compared with some of the existing approaches.
Abstract: With a rapid growth in smartphone technology, there is a need to provide secured access to critical data using personal authentication. Existing access mechanisms such as pin and password suffer due to lack of security from shoulder-surfing attacks. Use of biometric modalities such as fingerprint are currently explored in existing smartphones as a more secure authentication mechanism. Typically, fingerprint capturing requires an extra sensor, adding to the cost of the device as well as denying backend services to existing smartphone devices. Using a rear camera captured fingerphoto image provides a cheap alternate option, without the need for a dedicated sensor for capturing images. However, unlike fingerprints fingerphoto images are captured in a more uncontrolled environment including any outdoor conditions. Hence, fingerphoto matching is prone to many challenges such as varying environmental illumination and surrounding background. We propose a novel end-to-end fingerphoto matching pipeline by studying the effect of different environmental conditions in fingerphoto matching. The pipeline consists of the following major contributions: (i) a segmentation technique to extract the fingerphoto region of interest from varying background, (ii) an enhancement module to neutralize the illumination imbalance and increase the ridge–valley contrast, (iii) a scattering network based fingerphoto representation technique to deal with the pose variations, whose resultant features are invariant to geometric transformations, and (iv) a learning based matching technique to accommodate maximum variations occurring in fingerphoto images. To experimentally study the challenging conditions such as background and illumination, we create a publicly available fingerphoto dataset, IIITD SmartPhone Fingerphoto Database v1, along with the corresponding live-scan prints. The experiments performed on the dataset shows that the proposed matching pipeline provides an improved performance when compared with some of the existing approaches.
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TL;DR: This survey provides interested readers with a reasoned and systematic overview of problems, approaches, and available benchmarks forait biometrics and suggests continuing investigating.
Abstract: Gait is a biometric trait that can allow user authentication, though it is classified as a “soft” one due to a certain lack in permanence and to sensibility to specific conditions. The earliest research relies on computer vision, especially applied in video surveillance. More recently, the spread of wearable sensors, especially those embedded in mobile devices, has spurred a different research line. In fact, they are able to capture the dynamics of the walking pattern through simpler one-dimensional signals. This capture modality can avoid some problems related to computer vision-based techniques but suffers from specific limitations. Related research is still in a less advanced phase with respect to other biometric traits. However, many factors - the promising results achieved so far, the increasing accuracy of sensors, the ubiquitous presence of mobile devices, and the low cost of related techniques - contribute to making this biometrics attractive and suggest continuing investigating. This survey provides interested readers with a reasoned and systematic overview of problems, approaches, and available benchmarks.
72 citations
TL;DR: The proposed ellipsoid model is adaptive to both the silhouette of 2D contactless fingerprint image and the estimated view angle and can be theoretically estimated and incorporated to align two contactless fingerprints for achieving superior matching accuracy.
Abstract: Contactless fingerprint identification offers significantly higher user convenience, hygiene and has attracted increasing attention for the deployments. However, the presentation of fingers towards the contactless fingerprint sensors is hard to control and often results in unwanted pose changes that significantly degrade the contactless fingerprint matching accuracy. In order to address such problems and improve the fingerprint matching accuracy, this paper proposes $a$ more precise minutiae extraction and pose-compensation approach. As compared with the conventional minutiae extraction approaches, our deep neural network-based approach does not require any image enhancement and is robust to spurious minutiae. All the minutiae extracted from our network are subjected to $a$ three stage pose compensation framework: $a$ ) view angle estimation based on the location of core point, $b$ ) ellipsoid model formulation which simulates and compensate finger pose, $c$ ) intersection area estimation and alignment between different view angles. The proposed ellipsoid model is adaptive to both the silhouette of 2D contactless fingerprint image and the estimated view angle. The corresponding area between the different view angles can be theoretically estimated using this model and incorporated to align two contactless fingerprints for achieving superior matching accuracy. Our reproducible experimental results presented in this paper using public databases, and $a$ database acquired during this work, validate the effectiveness of the proposed framework over the commercial software and earlier methods.
33 citations
TL;DR: In this article , the FastAI technology is used with ResNet-32 model to precisely identify ductal carcinoma, and the proposed model has shown considerable efficiency in evaluating parameters like sensitivity, specificity, accuracy, and F1 Score against the other dominantly used deep learning models.
Abstract: Abstract Carcinoma is a primary source of morbidity in women globally, with metastatic disease accounting for most deaths. Its early discovery and diagnosis may significantly increase the odds of survival. Breast cancer imaging is critical for early identification, clinical staging, management choices, and treatment planning. In the current study, the FastAI technology is used with the ResNet-32 model to precisely identify ductal carcinoma. ResNet-32 is having few layers comparted to majority of its counterparts with almost identical performance. FastAI offers a rapid approximation toward the outcome for deep learning models via GPU acceleration and a faster callback mechanism, which would result in faster execution of the model with lesser code and yield better precision in classifying the tissue slides. Residual Network (ResNet) is proven to handle the vanishing gradient and effective feature learning better. Integration of two computationally efficient technologies has yielded a precision accuracy with reasonable computational efforts. The proposed model has shown considerable efficiency in the evaluating parameters like sensitivity, specificity, accuracy, and F1 Score against the other dominantly used deep learning models. These insights have shown that the proposed approach might assist practitioners in analyzing Breast Cancer (BC) cases appropriately, perhaps saving future complications and death. Clinical and pathological analysis and predictive accuracy have been improved with digital image processing.
29 citations
TL;DR: In this article, the state-of-the-art in the field of touchless 2D fingerprint recognition at each stage of the recognition process is summarized and technical considerations and trade-offs of the presented methods along with open issues and challenges.
Abstract: Touchless fingerprint recognition represents a rapidly growing field of research which has been studied for more than a decade Through a touchless acquisition process, many issues of touch-based systems are circumvented, eg, the presence of latent fingerprints or distortions caused by pressing fingers on a sensor surface However, touchless fingerprint recognition systems reveal new challenges In particular, a reliable detection and focusing of a presented finger as well as an appropriate preprocessing of the acquired finger image represent the most crucial tasks Also, further issues, eg, interoperability between touchless and touch-based fingerprints or presentation attack detection, are currently investigated by different research groups Many works have been proposed so far to put touchless fingerprint recognition into practice Published approaches range from self identification scenarios with commodity devices, eg, smartphones, to high performance on-the-move deployments paving the way for new fingerprint recognition application scenariosThis work summarizes the state-of-the-art in the field of touchless 2D fingerprint recognition at each stage of the recognition process Additionally, technical considerations and trade-offs of the presented methods are discussed along with open issues and challenges An overview of available research resources completes the work
27 citations
03 Jun 2020
TL;DR: An algorithm which comprises segmentation, enhancement, Deep Scattering Network based feature extraction, and Random Decision Forest to authenticate finger-selfies is proposed and results and comparison with existing algorithms show the efficacy of the proposed algorithm.
Abstract: With the advancements in technology, smartphones’ capabilities have increased immensely. For instance, the smartphone cameras are being used for face and ocular biometric-based authentication. This research proposes finger-selfie based authentication mechanism, which uses a smartphone camera to acquire a selfie of a finger. In addition to personal device-level authentication, finger-selfies may also be matched with livescan fingerprints present in the legacy/national ID databases for remote or touchless authentication. We propose an algorithm which comprises segmentation, enhancement, Deep Scattering Network based feature extraction, and Random Decision Forest to authenticate finger-selfies. This paper also presents one of the largest finger-selfie database with over 19, 400 images. The images in the IIIT-D Smartphone Finger-selfie Database v2 are captured using multiple smartphones and include variations due to background, illumination, resolution, and sensors. Results and comparison with existing algorithms show the efficacy of the proposed algorithm which yields equal error rates in the range of 2.1 – 5.2% for different experimental protocols.
23 citations
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