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All Journal Proceeding of The International Conference on Electrical Engineering and Informatics
Jonathan C Morano
University of the City of Manila
Electrical & Electronics Engineering

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An Enhancement of Harris Corner Detector Algorithm Applied in Signature Forgery Detection System Dzelle Faith R Tan; Pauline Regina J Obispo; Jonathan C Morano; Khatalyn E Mata
Proceeding of the International Conference on Electrical Engineering and Informatics Vol. 1 No. 1 (2024): January : Proceeding of the International Conference on Electrical Engineering
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/iceei.v1i1.27

Abstract

Signature verification is crucial for confirming the authenticity of identities in both administrative and financial transactions, where signature forgery can lead to significant security risks. The Harris Corner Detector Algorithm is a widely used method for feature extraction in image processing; its application spans various domains, such as detection of signature forgery. While effective in identifying key features, noise significantly affects performance, especially with impulse noise like salt-and-pepper noise commonly found in signature images. To solve this problem, this study enhances the Harris Corner Detector Algorithm by applying a median filter before gradient calculation. This method removes noise without sacrificing the integrity of key features important in signature forgery detection. The study evaluates the original and the enhanced algorithm using standard image quality metrics. Peak Signal-to-Noise Ratio (PSNR) surged from an average of 13.6 dB to 43.28 dB, the Structural Similarity Index (SSIM) improved significantly from 78% to 94%, and the Mean Squared Error (MSE) dropped substantially from 16.74 to 3.84. These advancements resulted in a more reliable algorithm, exhibiting excellent resistance to noise while maintaining image structure, making the enhanced algorithm highly effective for accurate signature forgery detection.
An Enhancement of Jiang, Z., et al.’s Compression-Based Classification Algorithm Applied to News Article Categorization Cid Antonio F Masapol; Sean Lester C Benavides; Jonathan C Morano; Khatalyn E Mata
Proceeding of the International Conference on Electrical Engineering and Informatics Vol. 1 No. 1 (2024): January : Proceeding of the International Conference on Electrical Engineering
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/iceei.v1i1.35

Abstract

This study enhances Jiang et al.'s compression-based classification algorithm by addressing its limitations in detecting semantic similarities between text documents. The proposed improvements focus on unigram extraction and optimized concatenation, eliminating reliance on entire document compression. By compressing extracted unigrams, the algorithm mitigates sliding window limitations inherent to gzip, improving compression efficiency and similarity detection. The optimized concatenation strategy replaces direct concatenation with the union of unigrams, reducing redundancy and enhancing the accuracy of Normalized Compression Distance (NCD) calculations. Experimental results across datasets of varying sizes and complexities demonstrate an average accuracy improvement of 5.73%, with gains of up to 11% on datasets containing longer documents. Notably, these improvements are more pronounced in datasets with high-label diversity and complex text structures. The methodology achieves these results while maintaining computational efficiency, making it suitable for resource-constrained environments. This study provides a robust, scalable solution for text classification, emphasizing lightweight preprocessing techniques to achieve efficient compression, which in turn enables more accurate classification.
Co-Authors Cid Antonio F Masapol Dzelle Faith R Tan Khatalyn E Mata Pauline Regina J Obispo Sean Lester C Benavides