A Review of Audio Features and Statistical Models Exploited for Voice Pattern Design

Audio fingerprinting, also named as audio hashing, has been well-known as a powerful technique to perform audio identification and synchronization. It basically involves two major steps: fingerprint (voice pattern) design and matching search. While the first step concerns the derivation of a robust and compact audio signature, the second step usually requires knowledge about database and quick-search algorithms. Though this technique offers a wide range of real-world applications, to the best of the authors' knowledge, a comprehensive survey of existing algorithms appeared more than eight years ago. Thus, in this paper, we present a more up-to-date review and, for emphasizing on the audio signal processing aspect, we focus our state-of-the-art survey on the fingerprint design step for which various audio features and their tractable statistical models are discussed.Comment: http://www.iaria.org/conferences2015/PATTERNS15.html ; Seventh International Conferences on Pervasive Patterns and Applications (PATTERNS 2015), Mar 2015, Nice, Franc

“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

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