A face recognition system based-ALMMo-0 classifier

Nowadays, biometric systems have emerged as a powerful tool for personal identification. Advanced research with significant results has been provided. Despite the important progress, there is a big need for improvement in the performance of security applications. More recently, an Autonomous Learning Multi-Model Classifier of 0- Order (ALMMo-0) has been proposed as a universal efficient tool of classification, autonomous, non-iterative, and fully explainable solving the problem of supervised pattern recognition. Thereby, our aim with this paper is to propose a new efficient methodology based on the ALMMo-0 classifier for authentication systems exploiting face modality. The proposed methodology is entirely data-driven, non-iterative, and feedforward. The proposed approach extracts the most relevant features from the face image by the Gabor filter bank descriptor, which is then fed into the ALMMo-0 classifier that extracts automatically the data clouds and builds its multimodal structure, forms its AnYa Fuzzy rule base (FRB) sub-classifiers for each class, generating objectively the score of confidence based on the mutual distribution then classify the new data using “winner takes all” strategy, as a result, the system decides whether the person is genuine or an imposter. Strong evidence of ALMMo-0 was found when experiments were conducted on nine face databases. Results were presented in the form of rank-1, equal error rate (EER), cumulative match curve (CMC), and receiver operating characteristic (ROC) curves. Furthermore, to provide more valuable information about our proposed system, results were also presented in the form of True Positive Rate (TPR) or the Genuine Acceptance Rate (GAR). The results demonstrated high performance of the proposed approach with not just a low error rate (EER) of 0.0% and a high accuracy (rank-1) of 100% but also high explainability and low computational complexity. Graphical abstract: (Figure presented.)