Fusion of face and iris biometrics in security verification systems.

Master of Science in Computer Science. University of KwaZulu-Natal, Durban, 2016.Abstract available in PDF file

Evaluating soft biometrics in the context of face recognition

2013 Summer.Includes bibliographical references.Soft biometrics typically refer to attributes of people such as their gender, the shape of their head, the color of their hair, etc. There is growing interest in soft biometrics as a means of improving automated face recognition since they hold the promise of significantly reducing recognition errors, in part by ruling out illogical choices. Here four experiments quantify performance gains on a difficult face recognition task when standard face recognition algorithms are augmented using information associated with soft biometrics. These experiments include a best-case analysis using perfect knowledge of gender and race, support vector machine-based soft biometric classifiers, face shape expressed through an active shape model, and finally appearance information from the image region directly surrounding the face. All four experiments indicate small improvements may be made when soft biometrics augment an existing algorithm. However, in all cases, the gains were modest. In the context of face recognition, empirical evidence suggests that significant gains using soft biometrics are hard to come by

A Review Of Multilevel Multibiometric Fusion System

Biometric systems allow automatic person recognition and authenticate based on the physical or behavioral characteristic. In recent years, researchers have paid close attention to the design of efficient multi-modal biometric systems due to their ability to withstand spoof attacks. Sometimes single biometric traits fail to extract relevant information for verifying the identity of a person. Therefore, combining multiple modalities, enhanced performance reliability could be achieved. If the security level increases then multi-level fusion techniques are used. This paper discusses the many fusion levels: algorithms, level of fusion, methods used for integrating the multiple verifiers and their applications

Ensemble of texture descriptors and classifiers for face recognition

Abstract Presented in this paper is a novel system for face recognition that works well in the wild and that is based on ensembles of descriptors that utilize different preprocessing techniques. The power of our proposed approach is demonstrated on two datasets: the FERET dataset and the Labeled Faces in the Wild (LFW) dataset. In the FERET datasets, where the aim is identification, we use the angle distance. In the LFW dataset, where the aim is to verify a given match, we use the Support Vector Machine and Similarity Metric Learning. Our proposed system performs well on both datasets, obtaining, to the best of our knowledge, one of the highest performance rates published in the literature on the FERET datasets. Particularly noteworthy is the fact that these good results on both datasets are obtained without using additional training patterns. The MATLAB source of our best ensemble approach will be freely available at https://www.dei.unipd.it/node/2357

Multimodal Behavioral Biometric Authentication in Smartphones for Covid-19 Pandemic

The usage of mobile phones has increased multi-fold in recent decades, mostly because of their utility in most aspects of daily life, such as communications, entertainment, and financial transactions. In use cases where users’ information is at risk from imposter attacks, biometrics-based authentication systems such as fingerprint or facial recognition are considered the most trustworthy in comparison to PIN, password, or pattern-based authentication systems in smartphones. Biometrics need to be presented at the time of power-on, they cannot be guessed or attacked through brute force and eliminate the possibility of shoulder surfing. However, fingerprints or facial recognition-based systems in smartphones may not be applicable in a pandemic situation like Covid-19, where hand gloves or face masks are mandatory to protect against unwanted exposure of the body parts. This paper investigates the situations in which fingerprints cannot be utilized due to hand gloves and hence presents an alternative biometric system using the multimodal Touchscreen swipe and Keystroke dynamics pattern. We propose a HandGlove mode of authentication where the system will automatically be triggered to authenticate a user based on Touchscreen swipe and Keystroke dynamics patterns. Our experimental results suggest that the proposed multimodal biometric system can operate with high accuracy. We experiment with different classifiers like Isolation Forest Classifier, SVM, k-NN Classifier, and fuzzy logic classifier with SVM to obtain the best authentication accuracy of 99.55% with 197 users on the Samsung Galaxy S20. We further study the problem of untrained external factors which can impact the user experience of authentication system and propose a model based on fuzzy logic to extend the functionality of the system to improve under novel external effects. In this experiment, we considered the untrained external factor of ‘sanitized hands’ with which the user tries to authenticate and achieved 93.5% accuracy in this scenario. The proposed multimodal system could be one of the most sought approaches for biometrics-based authentication in smartphones in a COVID-19 pandemic situation

Comparative Study And Analysis Of Quality Based Multibiometric Technique Using Fuzzy Inference System

Biometric is a science and technology of measuring and analyzing biological data i.e. physical or behavioral traits which is able to uniquely recognize a person from others. Prior studies of biometric verification systems with fusion of several biometric sources have been proved to be outstanding over single biometric system. However, fusion approach without considering the quality information of the data used will affect the system performance where in some cases the performances of the fusion system may become worse compared to the performances of either one of the single systems. In order to overcome this limitation, this study proposes a quality based fusion scheme by designing a fuzzy inference system (FIS) which is able to determine the optimum weight to combine the parameter for fusion systems in changing conditions. For this purpose, fusion systems which combine two modalities i.e. speech and lip traits are experimented. For speech signal, Mel Frequency Cepstral Coefficient (MFCC) is used as features while region of interest (ROI) of lip image is employed as lip features. Support vector machine (SVM) is then executed as classifier to the verification system. For validation, common fusion schemes i.e. minimum rule, maximum rule, simple sum rule, weighted sum rule are compared to the proposed quality based fusion scheme. From the experimental results at 35dB SNR of speech and 0.8 quality density of lip, the EER percentages for speech, lip, minimum rule, maximum rule, simple sum rule, weighted sum rule systems are observed as 5.9210%, 37.2157%, 33.2676%, 31.1364%, 4.0112% and 14.9023%, respectively compared to the performances of sugeno-type FIS and mamdani-type FIS i.e. 1.9974% and 1.9745%