Feature Ranking Using Novel Consistency Measure by Normalized Standard Deviation and Proposal of Three Novel Global Features for Online Signature Verification

Biswajit Kar; Bhimraj Prasai Chetry; Sayan Das

doi:10.52756/ijerr.2024.v44spl.016

Authors

Biswajit Kar Department of Instrumentation Engineering, Central Institute of Technology Kokrajhar, Assam, India https://orcid.org/0000-0003-2686-5814
Bhimraj Prasai Chetry Department of Instrumentation Engineering, Central Institute of Technology Kokrajhar, Assam, India https://orcid.org/0009-0002-5072-4739
Sayan Das Department of Instrumentation Engineering, Central Institute of Technology Kokrajhar, Assam, India https://orcid.org/0009-0006-4644-9438

DOI:

https://doi.org/10.52756/ijerr.2024.v44spl.016

Keywords:

Consistency estimation, Feature extraction, Feature normalization, Feature ranking, Feature selection, mRMR

Abstract

Signature is a behavioral biometric that evolves throughout a person’s life. Feature extraction and ranking are very important steps towards online signature verification in order to achieve high efficiency. In our case, we have extracted 48 global features. A novel feature ranking technique based on consistency measure using normalized standard deviation is proposed here and is compared with well-established mRMR based ranking. The use of normalized standard deviation formula is the novel approach for feature ranking. Moreover, we have proposed three novel global features for consistency measure and mRMR based ranking. These three features has shown its importance in consistency based ranking as well as in mRMR based ranking. Consistency estimation of global features is important in one-class classification framework, where only the genuine signatures are available. All the features that are ranked by consistency measures and its weighting factors of feature vector are computed for every signer. More consistent features has given more weight for verification. Feature ranking shows the importance of each features for a particular system. Also it helps in feature selection to select the more discriminating features for a particular system and removing all irrelevant features. It saves the computational time and size of the model. Different global features has different scaling factor. Generally, normalization is done before ranking but in our technique we have ranked our features without normalization. Because normalization disrupt the statistical consistency. Therefore, we have used min-max normalization where all the features are converted to (0-1) range after ranking. In our proposed system, the proposed phase related global features shows more consistency in our ranking process. Similarly well-established mRMR feature ranking has also ranked our proposed novel features number 48 (Standard deviation of the phase), 43 (Entropy of shape signature function) and 47 (Mean of phase) within top 13 features. It is seen that both our proposed novel feature ranking technique based on consistency measure and well-established mRMR based ranking has shown almost similar performance. The proposed algorithms are verified with SVC 2004 database.

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