Electro‐optic effects in nematic liquid crystals

Experimental results on the electro-optic effects in nematic liquid crystals are presented. A hexagonal grid pattern is observed with (ac + dc) field at a critical frequency f(c) (168 Hz). The time required for the formation of the grid pattern with applied field is estimated

Identify and Rectify the Distorted Fingerprints

Elastic distortion of fingerprints is the major causes for false non-match. While this cause disturbs all fingerprint recognition applications, it is especiallyrisk in negative recognition applications, such as watch list and deduplication applications. In such applications, malicious persons may consciously distort their fingerprints to hide identification. In this paper, we suggested novel algorithms to detect and rectify skin distortion based on a single fingerprint image. Distortion detection is displayed as a two-class categorization problem, for which the registered ridge orientation map and period map of a fingerprint are beneficial as the feature vector and a SVM classifier is trained to act the classification task. Distortion rectification (or equivalently distortion field estimation) is viewed as a regression complication, where the input is a distorted fingerprint and the output is the distortion field. To clarify this problem, a database (called reference database) of various distorted reference fingerprints and corresponding distortion fields is built in the offline stage, and then in the online stage, the closest neighbor of the input fingerprint is organized in the reference database and the corresponding distortion field is used to transform (Convert) the input fingerprint into a normal fingerprints. Promising results have been obtained on three databases having many distorted fingerprints, namely FVC2004 DB1, Tsinghua Distorted Fingerprint database, and the NIST SD27 latent fingerprint database

“Implementation on Distorted Fingerprints”

Flexible distortion of fingerprints is the main origin of false non-match. While this origin disturbs all fingerprint recognition applications, it is mainly risk in negative recognition applications, such as watch list duplication applications. In such things, malignant user mayconsciously distort their fingerprints to hide his originality or identification. This paper, suggested novel algorithms to identify and modify skin distortion based on a single fingerprint image. Distortion detection is displayed as a two-class categorization problem, for which the registered ridge orientation map and period map of a fingerprint are beneficial as the feature vector and a SVM classifier is trained to act the classification task. Distortion rectification (or equivalently distortion field estimation) is viewed as a regression complication, where provide the input as a distorted fingerprint and generate the output as distortion field. To clarify this Problem, offline and online stages are important. A database (called reference database) of various distorted reference fingerprints and corresponding distortion fields is built in the offline stage, and then in the online stage, the closest neighbor of the input fingerprint is organized in the reference database and the corresponding distortion field is used to transform (Convert) the input distorted fingerprint into a normal undistorted fingerprints

Mixing Biometric Data For Generating Joint Identities and Preserving Privacy

Biometrics is the science of automatically recognizing individuals by utilizing biological traits such as fingerprints, face, iris and voice. A classical biometric system digitizes the human body and uses this digitized identity for human recognition. In this work, we introduce the concept of mixing biometrics. Mixing biometrics refers to the process of generating a new biometric image by fusing images of different fingers, different faces, or different irises. The resultant mixed image can be used directly in the feature extraction and matching stages of an existing biometric system. In this regard, we design and systematically evaluate novel methods for generating mixed images for the fingerprint, iris and face modalities. Further, we extend the concept of mixing to accommodate two distinct modalities of an individual, viz., fingerprint and iris. The utility of mixing biometrics is demonstrated in two different applications. The first application deals with the issue of generating a joint digital identity. A joint identity inherits its uniqueness from two or more individuals and can be used in scenarios such as joint bank accounts or two-man rule systems. The second application deals with the issue of biometric privacy, where the concept of mixing is used for de-identifying or obscuring biometric images and for generating cancelable biometrics. Extensive experimental analysis suggests that the concept of biometric mixing has several benefits and can be easily incorporated into existing biometric systems