Hybrid LBP–HOG Feature Extraction with Support Vector Machine and Deep Belief Network for Offline Signature Identification and Verification
DOI:
https://doi.org/10.54082/jiki.350Keywords:
Deep Belief Network, Image Processing, Pattern Recognition, Signature, Support Vector MachineAbstract
Offline signature verification remains a challenging task due to high intra-writer variability and the presence of skilled forgeries, which can reduce the reliability of biometric authentication systems. This study aims to develop a robust offline signature identification and verification framework by integrating hybrid feature extraction and machine learning-based classification methods. The proposed approach combines Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG) to capture complementary texture and shape characteristics from signature images. The extracted features are subsequently classified using Support Vector Machine (SVM) for writer identification and Deep Belief Network (DBN) for signature verification. The experimental dataset consists of signatures collected from 60 individuals, with each participant providing 8 genuine and 8 forged signatures. Prior to feature extraction, preprocessing stages including grayscale conversion, contrast enhancement, and Otsu thresholding were applied to improve image quality and segmentation consistency. Experimental results demonstrate that the proposed framework achieves an identification accuracy of 89.58% using SVM, while the DBN-based verification model attains an accuracy of 87%, an F1-score of 86%, and an AUC value of 0.85. The novelty of this study lies in the integration of hybrid LBP–HOG feature extraction with a dual-classification architecture combining SVM and DBN for offline signature authentication tasks. The combination enables the system to effectively represent both local texture patterns and structural signature characteristics while improving classification robustness against forged signatures. These findings indicate that the proposed framework provides a promising contribution to offline biometric authentication and computer vision-based signature verification research.
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