Signature Identification Based on GLCM Feature Extraction and Convolutional Neural Network Classification - Lailan Sofinah Harahap; Haliza Suci Rachmadini (Forum Kerjasama Pendidikan Tinggi (FKPT), 2026)
Signature is one of the widely used biometric features for authentication and identity verification purposes. This study proposes a digital signature identification system by combining Gray Level Co-occurrence Matrix (GLCM) feature extraction and Convolutional Neural Network (CNN) classification. The dataset consists of 500 signature images from 50 individuals collected independently. Preprocessing steps include grayscale conversion, adaptive binarization, and image normalization to 128×128 pixels. GLCM texture features are extracted at four angular directions (0°, 45°, 90°, 135°) yielding five main features: contrast, correlation, energy, homogeneity, and entropy. These features are integrated as additional inputs to the fully connected layer of a CNN comprising three convolutional blocks. Experimental results demonstrate that the proposed system achieves a classification accuracy of 96.8%, precision of 96.2%, and F1-Score of 96.5% on test data. These results confirm that integrating GLCM texture features into the CNN architecture significantly improves signature identification performance.