METHOD AND SYSTEM TO CENTRALLY MONITOR THE QUALITY OF IMAGES OF FINANCIAL DOCUMENTSDate:2021-09-17
The financial implications of welfare fraud in the state of Pennsylvania
In today's computerized society, impostors are gaining access when compared to real authorized users. Spoofing biometric systems to gain unauthorized access is the main concept of impostor. Spoofing is the process of defeating a biometric system through the introduction of fake biometric samples. It is a method of fooling biometric system, where fake objects are presented to the scanner which imitates unique properties as authenticators, thereby fooling it from distinguishing artifact from real target People leave finger prints everywhere in day to day lives. Faces and irises are visible to everyone and voices can be recorded. So, the chance of someone lifting and copying them to replicate for the purpose of spoofing may be possible. Hence it is considered as a challenging threat to Biometric systems. Liveness Detection is considered as the most and spoofing method for protecting genuine authentications from impostors. It is done mainly under three categories: Intrinsic properties of living body, involuntary signals of living body, and Bodily responses to external stimuli Eye blinks and pupil dynamics are considered for morphological traits of Iris, and characteristics like red eye effect, Purkinje images etc are mentioned for dynamics liveness detection. In this paper, target biometric traits for spoofing are mentioned and various methods of liveness detection are explained. Pupil dilations are registered under various intensities of visible light and a feature vector is developed, measuring the distances between the movement's i.e. inner boundary and outer boundary of the image. Since the templates developed results in different values two groups of classifications are done, one is authentic and the other is fake. So, in order to achieve some values for fake classifications, some fake objects are also considered for results. Oscillations of pupil under different intensities of visible light results in this liveness detection
Biometrics is attracting increasing attention in privacy and security concerned issues, such as access control and remote financial transaction. However, advanced forgery and spoofing techniques are threatening the reliability of conventional biometric modalities. This has been motivating our investigation of a novel yet promising modality transient evoked otoacoustic emission (TEOAE), which is an acoustic response generated from cochlea after a click stimulus. Unlike conventional modalities that are easily accessible or captured, TEOAE is naturally immune to replay and falsification attacks as a physiological outcome from human auditory system. In this paper, we resort to wavelet analysis to derive the time-frequency representation of such nonstationary signal, which reveals individual uniqueness and long-term reproducibility. A machine learning technique linear discriminant analysis is subsequently utilized to reduce intrasubject variability and further capture intersubject differentiation features. Considering practical application, we also introduce a complete framework of the biometric system in both verification and identification modes. Comparative experiments on a TEOAE data set of biometric setting show the merits of the proposed method. Performance is further improved with fusion of information from both ears.
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