A texture feature based approach for person verification using footprint bio-metric

• Published in: The Artificial intelligence review 2021-02, Vol.54 (2), p.1581-1611
• Main Authors: Kushwaha, Riti, Singal, Gaurav, Nain, Neeta
• Format: Article
• Language: English
• Subjects: Accuracy; Analysis; Artificial Intelligence; Biometrics; Biometry; Centroids; Classification; Computer Science; Discriminant analysis; Feature extraction; Footprints; Histograms; Support vector machines; Texture; Uniqueness; Verification
• ISSN: 0269-2821; 1573-7462
• DOI: 10.1007/s10462-020-09887-6

Biometrics is the study of unique characteristics present in the human body such as fingerprint, palm-print, retina, iris, footprint, etc. While other traits have been explored widely, only a few people have been considered the foot-palm region, despite having unique properties. Prior work has explored the foot shape features using length, width, major axis, minor axis, centroid, etc. but they are not reliable for personal verification due to similarity in the physical composition of two persons. It increases the demand for more unique features based on the footprint. Footprint texture features coming from creases of foot palm are unique and permanent like palmprint texture features. Hence the main objective of the paper is to investigate various kinds of texture feature techniques. These techniques will be further used in correct extraction of footprint features. After extraction of footprint features a detailed experimental analysis is performed to discover the uniqueness in foot texture. It is further utilized to test its viability as a human recognition trait. We describe a detailed feature extraction and classification technique applied to a collected footprint data-set. For feature extraction, we use three techniques: Gray Level Co-occurrence Matrix ( GLCM ), Histogram Oriented Gradient ( HOG ), and Local Binary Patterns ( LBP ). Feature classification is performed using four techniques: Linear Discriminant Analysis ( LDA ), Support Vector Machine ( SVM ), K-Nearest Neighbor ( KNN ), and Ensemble Subspace Discriminant ( ESD ). GLCM provides less accuracy, while HOG generates a big feature vector which takes more execution time. LBP provides a trade-off between the accuracy and the execution time. Detailed quantitative experiments show: GLCM with LDA provides an accuracy of 88.5 % , HOG with Fine- KNN achieves 86.5 % accuracy and LBP with LDA achieves the accuracy of 97.9 % .