Palm print recognition using competitive hand valley detection, local binary pattern and probabilistic neural network

Abstract

Biometric is a method to iden the specific feature based on part Nowadays, biometric is widely used performance to identify human. The pa used in this research were manually coll of 110 people with 10 images for each p recognition system consist of : region extraction using Competitive Hand (CHVD) method, feature extraction pro Binary Pattern (LBP) method and clas using Probabilistic Neural Network research we proposed area segmentati LBP that consist of overlap and non-ove using segmentation the feature will beca and distinctive. In 60 people (50 known the best system accuracy achieve was 9 110 people (100 known and 10 unknown) achieve was 91.91%. The best performan with four overlap segment schemas, 8 che smoothing parameter set at 0.03.

A novel biometric approach to generate ROC curve from the Probabilistic Neural Network

“…It is just for an MLP with a bias and only two classes Using commercial software [3] Yes for PNN with only two classes. The curve was not smooth The results were averaged [4] Yes after running the PNN many times A combination method [5] Yes using a PPNN and a Gabor filter A combination method [6] Yes using a PPNN and kernel discriminant analysis A combination of multiple [7] Yes neural networks then applying a voting process [8], [9], [10], [11], [12], [13], [14], [15], No - [16], [17] and [18]…”

Untitled

“…Teknologi biometrika tidak mungkin dapat diduplikasi atau terlupa karena proses dari teknologi tersebut menggunakan bagian tubuh untuk dikenali identitasnya dan mengharuskan seseorang untuk hadir atau tidak dapat diwakilkan [1]. [11]. Pengolahan citra adalah suatu sistem pemroses khusus dengan menggunakan media komputer yang masukkannya berupa citra digital, agar menjadi keluaran citra yang kualitasnya lebih baik [12].…”