Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

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.

https://dl.acm.org/doi/pdf/10.1145/3340293

A Survey on Gait Recognition via Wearable Sensors

Fingerprint technology has evolved immensely since its initial use in the 1800s when it was used solely to assist with crime investigations. It is now used as a convenience replacing passwords and PIN numbers from logging into bank accounts, mobile devices, gaining access into rooms and various other processes where time plays a key factor. This chapter focuses on how fingerprint technology can be used to improve speed and accuracy of certain processes, i.e. exams as the society accepts this as part of everyday life. In particular, we look at a use case in an educational setting where youngsters are much used to digital technologies as part of their daily life.

Advances in Fingerprint Technology

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.

Towards More Accurate Contactless Fingerprint Minutiae Extraction and Pose-Invariant Matching

Resource depletion and environmental issues persuade manufacturers to reuse and recycle electronic products. One green strategy in mobile device manufacturing is to refurbish and reuse these products. Consumer feedback in the secondary market impress decision-makers and manufacturer strategies. This research provides a framework that focuses on data mining techniques to investigate consumer attitudes toward refurbished phones by one of the reverse supply chain practices: refurbishment. Furthermore, the consumer's opinions on refurbished mobile phones found on Twitter have been analyzed, and the most appropriate selling strategy has been proposed. Accordingly, the customer's strong motivating factors were identified by assessing approximately 25,000 tweets. The obtained data are analyzed according to three categories: environmental versus financial motivators, refurbished mobile phone features, and main components of refurbished mobile phones. The results indicate that the environmental factor is slightly higher motivating when compared to the financial factor. Other important features were prioritized from the consumer's point of view (highest to lowest): price, product warranty, quality, and seller's reputation. In a separate analysis, camera, display, battery, performance, innovative technologies, and internal memory were found to be the top components of a mobile device that customers pay attention to the most. Interrelationships between identified features were established using interpretive structural modeling (ISM) to accomplish a comprehensive framework. Classification analysis was performed by the matrix of cross-impact multiplications applied to classifications (MICMAC) technique to identify these features based on their driving and dependence power. Camera, screen, battery, internal storage, and performance were significant features that affected customer's attitudes.

Promoting consumer's attitude toward refurbished mobile phones: A social media analytics approach

Authenticating fingerphoto images captured using a smartphone camera, provide a good alternate solution in place of traditional pin or pattern based approaches. There are multiple challenges associated with fingerphoto authentication such as background variations, environmental illumination, estimating finger position, and camera resolution. In this research, we propose a novel ScatNet feature based fingerphoto matching approach. Effective fingerphoto segmentation and enhancement are performed to aid the matching process and to attenuate the effect of capture variations. Further, we propose and create a publicly available smartphone fingerphoto database having three different subsets addressing the challenges of environmental illumination and background, along with their corresponding live scan fingerprints. Experimental results show improved performance across multiple challenges present in the database.

On smartphone camera based fingerphoto authentication

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.

Apoorva Mittal

Papers

On smartphone camera based fingerphoto authentication

Fingerphoto Authentication Using Smartphone Camera Captured Under Varying Environmental Conditions