Open AccessSignature Texture Features Extraction Using GLCM Approach in Android Studio
Author(s) -
Hussein Awad Dwaich,
Huda Abdulaali Abdulbaqi
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1804/1/012043
Subject(s) - computer science , feature extraction , artificial intelligence , android (operating system) , pattern recognition (psychology) , image texture , entropy (arrow of time) , computer vision , image processing , image (mathematics) , physics , quantum mechanics , operating system
Signature Features Extraction is a method for deriving informative values of image signature for indexing and signature identification. It is a dimensionality reduction of image data to be manageable for processing. Image texture is a key spatial attribute used for feature extraction and image coding. However, Android environment lack of efficient algorithm for automatic extracting texture features which may cause serious security issues and unreliability problem in the Android application (app). The challenge in Signature Features Extraction of a mobile app is to be as robust and stable as possible. For that, the present paper presents an efficient algorithm for extracting signature texture features using a gray level co-occurrence matrix (GLCM). The image signature is quantized into five texture features of energy feature, entropy feature, contrast feature, dissimilarity feature, and homogeneity feature. The processes developed in the android studio environment to be used in mobile phone cell applications as a tool for signature detection. The results show that the present method provides significant results due to obtained average values and acceptable computational time.