Personal palm vein identification using principal component analysis and probabilistic neural network

Biometric is one type of physical characteristics that can be used for person identification as it has uniqueness. Palm vein is one of individual biometrics that attracts researcher attention recently. The advantages of palm vein compared to others are that palm vein represent if someone is still alive or dead, palm vein is hidden under the human skin, and palm vein is quite impossible to be imitated. However, a study related to the application of biometric identification based on palm vein is still rarely done. Several methods and the implementations need to be observed, while the performances have to be proved as well. This study is aimed to apply Principal Component Analysis (PCA) as the feature extraction method and Probabilistic Neural Network (PNN) as the classification method. PCA is capable to reduce the feature dimension size, but still maintaining the important feature. So, the computational load is getting faster without damaging the extracted features. Meanwhile, PNN is a classification method that is known as a quick method in the training process but still optimizing the performance. The data set used in this research comes from Casia Database Multispectral. Some observations are done to obtain the optimal parameters setting. The highest accuracy obtained during the testing phase is 84% with the feature length used is 180 and the value of g is 0.001 for classifying 50 distinct individuals.

[1]  Qin Li,et al.  Palm Vein Extraction and Matching for Personal Authentication , 2007, VISUAL.

[2]  Andrew Beng Jin Teoh,et al.  Touch-less palm print biometrics: Novel design and implementation , 2008, Image Vis. Comput..

[3]  Suyanto.  Artificial intelligence : searching, reasoning planning dan learning , 2014 .

[4]  Wee Ser,et al.  Probabilistic neural-network structure determination for pattern classification , 2000, IEEE Trans. Neural Networks Learn. Syst..

[5]  Helen C. Shen,et al.  Recognition of Palmprints using Eigenpalm , 2003 .

[6]  David Zhang,et al.  Palmprint recognition using eigenpalms features , 2003, Pattern Recognit. Lett..

[7]  Prasti Eko Yunanto.  PENGENALAN TELAPAK TANGAN MELALUI CITRA DIGITAL DENGAN MENGGUNAKAN METODE EIGENPALM DAN SUPPORT VECTOR MACHINE , 2012 .

[8]  Ajay Kumar,et al.  Human Identification Using Palm-Vein Images , 2011, IEEE Transactions on Information Forensics and Security.

[9]  Mircea Nicolescu,et al.  Hand-based verification and identification using palm-finger segmentation and fusion , 2009, Comput. Vis. Image Underst..

[10]  Mario Baum,et al.  Handbook Of Biometrics , 2016 .

[11]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[12]  Dwi Arliani.  Analisis Pengenalan Wajah Hasil Akuisisi Webcam menggunakan Metode Eigenface dan Support Vector Machine , 2011 .

[13]  Yuxuan Wang,et al.  A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network , 2007, 2007 IEEE International Symposium on Signal Processing and Information Technology.

[14]  Helen C. Shen,et al.  Recognition of palmprints using eigenpalms , 2003, CVPR 2003.