Enhanced multimodal biometric access control system using chicken swarm optimization and self-organizing feature maps: a study on ear and iris recognition

Access control systems are crucial for securing sensitive data and system components by allowing authorized access while blocking unauthorized entities. Traditional unimodal biometric systems, make use of a single physiological or behavioral trait, have limitations such as susceptibility to spoofing and environmental constraints, leading to reduced reliability. This study explores an enhanced multimodal biometric access control system that combines ear and iris traits using an enhanced Self-Organizing Feature Map (SOFM) algorithm improved with Chicken Swarm Optimization (CSO). The system's performance is evaluated against traditional SOFM, with a focus on recognition accuracy and processing time. The data used to train the classifier for this study were collected from 190 individuals, encompassing a total of 2,280 images of iris, and ear traits. Preprocessing involved cropping, resizing, and grayscale conversion using histogram equalization. Feature extraction utilized Local Binary Patterns (LBP), followed by feature fusion at the feature level to create an integrated feature set. The enhanced SOFM algorithm was then applied for classification, with the CSO technique optimizing the learning rate and weight parameters for improved performance. At different thresholds, the CSO-SOFM classifier outperformed the standard SOFM classifier using metrics such as Sensitivity, Specificity, Precision, Accuracy and Recognition time.