Biometric Face Identification: Utilizing Soft Computing Methods for Feature-Based Recognition

Biometric facial identification presents a distinct and reliable method for distinguishing individuals based on unique physical or behavioral characteristics. Unlike traditional security measures such as passwords, facial features offer a level of security that cannot be shared, replicated, or forgotten. This study focuses on the application of facial biometrics for person identification, leveraging the advantages of non-contact biometrics like facial features over other methods such as fingerprint or palm recognition. Facial recognition in this work is predicated on the geometric shapes or facial characteristics. Emphasis is placed on three fundamental views of the face: upward, frontal, and downward. For each of these views, specific regions are extracted for processing, including the right-eye region and its width. Simultaneously, the dimensions of the mouth, both height and width, are extracted in a similar manner. Training and evaluation of the proposed system are accomplished using three soft computing models: an Artificial Neural Network (ANN), a Particle Swarm Optimization Neural Network (PSO-NN) model, and an Adaptive Neuro-Fuzzy Inference System (ANFIS) model. Each model employs a dataset constructed for each view. Optimization of the models is achieved by adjusting parameters like the number of neurons used in the hidden layer for recognition in neural network-based procedures. Performance evaluation of the proposed system is conducted by computing the mean square error, obtained by random data division. The models demonstrated a training set accuracy of 97.20% and a testing data set accuracy of 90.86%. These results indicate the effectiveness of the proposed system for both individual and combined face views, underscoring the potential of facial biometrics in secure identification applications.