Development Of Iris Based Age And Gender Detection System

Biometric recognition has shown their utility in different aspect including reducing search space significantly which has led to improve recognition performance, reduced computation time and faster processing time, a person is identified automatically by processing the unique features that are posed by the individual. The existing biometric systems recognize and accept a person, else simply reject a person if not enrolled, but these systems do not give software attributes of a person which is needed to search impostor as it has been observed in many occasions and especially in Nigeria that identification of an individual’s age and gender does not go beyond physical factors which in most cases can be manipulated or spoofed to carry out nefarious activities. This project focuses on developing an iris-based age and gender detection system for certain individuals to identify a person in real time. Left and right irises of 190 subjects constituting 1,716 images were acquired and Five Hundred and Seventy (570) left Irises were normalized to a uniform size of 250 by 250 pixels. Three Hundred and Forty-Two (342) images were used for training while the remaining Two Hundred and Twenty-Eight (228) were used for testing. The acquired images were preprocessed by performing segmentation, filtering and normalization using Daugman’s Rubber Sheet Model. Deep learning pre-trained networks are adopted to extract features from iris images. Further, these features are trained and classified using the multi-class Support Vector Machines (SVM) model for performance evaluation of the system. The system was implemented in Matrix Laboratory 9.0 (R2016a). The performance of the system was evaluated using accuracy, precision, recall and False Positive Rate (FPR). The hypothesis stating ‘the iris has age, and gender-related information’ is proven correct from experimental results. The evaluation results showed that False Positive Rate, Recall, Precision and Accuracy of gender prediction were 2.19%, 92.31%, 96.55% and 95.62% respectively at 0.75 threshold while for age prediction, the best values were obtained at age group of 20-24 for FPR, Recall, Precision and Accuracy which were 5.61%, 94.37%, 96.18% and 94.38% respectively. The developed model gave good performance with high recognition accuracy, recall, precision and low FPR values. Therefore, the developed model can be used in all firms and industries where security and personal identification is desired for security purpose and in going through investigation of criminal records for detecting age and gender of individuals.