Face recognition system has been foremost since the advent of computers as it involves low-cost installation, non-invasive equipment, and the ease of data collection process. However, the development of face recognition system is not straightforward due to the changes of facial expression, face feature and pose. These changes lead to the performance degradation. Therefore, a multibiometric system is explored and an adaptive decision weighted fusion-based strategy is proposed to tackle the poor performance of single biometric system. The proposed strategy is divided into two parts. In part one, the adaptive weighted is considered where the weighted value is determine based on distance metric of Euclidean Distance (ED). In part two, the decision of fusion process is employed to select the optimum weightage based on distance that obtained in part one. Two feature extractions which are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) have been employed to investigate the effectiveness of proposed fusion technique in face identification process. In order to evaluate the effectiveness of the proposed strategy, the face image of benchmark database and collected database are used for the performance evaluations. The results show an adaptive decision weighted fusion-based strategy comes out as an outstanding fusion technique compared to other tested rules with the performances of 95.33% is achieved from the benchmark database and collected database, respectively.
