AI-Based Palm Print Recognition System for High-security Applications

In recent years, many studies have failed to implement an effective palm print recognition system for high-security applications. This study focuses on developing a novel palm print recognition system using novel data processing techniques. The study proposes an embedded zero-tree wavelet (EZW) and principal component analysis (PCA) feature extraction technique concerning palm print recognition. The database contains palm print image samples from right and left palm images. 200 images of 5 people were captured with each person, and 40 shots were used. 150 images were used in the SVM training, and 50 images were used in the SVM testing. The spectral feature extraction of the palm print image is processed by the EZW. The spatial feature extraction of the palm print image is processed by PCA. The minimum distance classifier is used for the comparison of results. Finally, the palm print images are trained and classified with Support Vector Machine (SVM). The researcher concluded that, when compared to the other evaluated approaches and classifiers, the palm print recognition system that combines EZW and PCA as a method of feature extraction is the most accurate. The overall testing results show that the proposed approach yields a maximum of 90.4% recognition accuracy.