Trimodal biometric security systems using deep learning technique

Biometrics refers to the automatic recognition of individuals based on their physiological and behavioural characteristics. These characteristics are unique to each individual and remain unaltered throughout human lifetime. Several unimodal, bimodal and trimodal biometric security systems have been developed using convolutional neural network but few of them have been able to handle the challenges of accurate recognition rates and processing time. In this work, a comparative study of the performances of unimodal, bimodal and trimodal biometric security systems using deep learning technique was carried out. The System was tested on a database consisting of 1026 trained images and 684 probe images of face, ear and iris biometrics. All the images were preprocessed. Feature extraction and classification were carried out using deep learning technique, precisely, Convolutional Neural Network Algorithm (CNN). The results show that the unimodal, the Ear system produced highest value of 91.67% accuracy, Sensitivity of 93.57%, Specificity of 85.96%, Precision of 95.24% in in 114.10secs time. In bimodal system Ear-Iris produced highest value of 96.05 accuracy, Sensitivity of 96.49%, Specificity of 94.74%, Precision of 98.21% in 297.01 time, while the developed system propduced Sensitivity of 97.66%, Specificity of 98.25%, Precision of 99.40%, Recognition Accuracy of 97.81% but the Recognition Time of 455.54 Secs.