Comparative assessment of back propagation neural network and self organising feature map for iris-based access control system

Iris recognition is considered as one of the best biometric methods used for human identification and verification because of its unique features that differ from one person to another. Self-Organizing Feature Map (SOFM) and Back Propagation Neural Network (BPNN) are two techniques that have been used previously for iris recognition but their performance comparison has not been adequately investigated. This research, therefore, carried out a comparative analysis between SOFM and BPNN in iris recognition. Three hundred (300) iris images from one hundred and fifty (150) subjects were acquired from Covenant University Iris dataset. The acquired iris images were pre-processed by segmenting the iris portion using Hough Transform and were normalized using Daugman’s Rubber Sheet Model. Local Binary Pattern was used for feature extraction and dimension reduction of the iris images. Each of the two algorithms, SOFM and BPNN was used individually as iris image classifier, this technique was implemented using MATLAB (R2016a). The performance of the two iris classifiers was evaluated individually and compared at 0.75 threshold value based on Recognition Accuracy (RA), False Acceptance Rate (FAR), False Rejection Rate (FRR), Equal Error Rate (EER), Training Time (TT) and Recognition Time (RT). The SOFM and BPNN techniques were validated by carrying out a t-test to compare the differences between the two techniques at 5% significance level. The SOFM technique gave RA, FAR, FRR, EER, TT and RT of 97.14%, 1.67%, 3.75%, 3.18%, 104.47s and 81.98s, respectively, while the corresponding values for BPNN were 94.29%, 5.00%, 6.25%, 5.91%, 116.23s and 101.77s respectively. The P value between the SOFM and BPNN techniques was 0.002. This research outcome revealed that SOFM outperformed BPNN in iris recognition system with respect to RA, FAR, FRR, EER, TT and RT. The SOFM technique could be used for more robust iris recognition system than BPNN in security surveillance systems or other related systems.