Language Identification by Using SIFT Features
نویسندگان
چکیده
منابع مشابه
Advanced Touchless Biometric Identification using SIFT Features
The motto of this paper is to provide an efficient Biometric Identification using Multimodal Touchless Finger Print Acquisition and Mosaicking Technique. Fingerprints are traditionally captured based on contact of the finger on paper or a platen surface. This often results in partial or degraded images due to improper finger placement, skin deformation, slippage and smearing, or sensor noise fr...
متن کاملFingerprint Verification Using SIFT Features
Fingerprints are being extensively used for person identification in a number of commercial, civil, and forensic applications. Most of the current fingerprint verification systems utilize features that are based on minutiae points and ridge patterns. While minutiae based fingerprint verification systems have shown fairly high accuracies, further improvements in their performance are needed for ...
متن کاملFace Recognition using SIFT Features
Face recognition has many important practical applications, like surveillance and access control. It is concerned with the problem of correctly identifying face images and assigning them to persons in a database. This paper proposes using SIFT features [4] for the recognition process. The new technique is compared with well-established face recognition algorithms, namely Eigenfaces [7] and Fish...
متن کاملSVD-matching using SIFT features
The paper tackles the problem of feature points matching between pair of images of the same scene. This is a key problem in computer vision. The method we discuss here is a version of the SVD-matching proposed by Scott and LonguetHiggins and later modified by Pilu, that we elaborate in order to cope with large scale variations. To this end we add to the feature detection phase a keypoint descri...
متن کاملLanguage Identification Using Excitation Source Features
This book discusses the contribution of excitation source information in discriminating language. The authors focus on the excitation source component of speech for enhancement of language identification (LID) performance. Language specific features are extracted using two different modes: (i) Implicit processing of linear prediction (LP) residual and (ii) Explicit parameterization of linear pr...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Advanced Research in Artificial Intelligence
سال: 2015
ISSN: 2165-4069,2165-4050
DOI: 10.14569/ijarai.2015.041206