Face recognition refers to the process of identifying individuals based on their facial features. It has recently become one of the most popular research areas in the fields of computer vision, machine learning, and pattern recognition because it spans numerous consumer applications, such as access control, surveillance, security, creditcard verification, and criminal identification. However, illumination variation on face generally cause performance degradation of face recognition systems under practical environments. Thus, this paper proposes an novel face recognition system using a fusion approach based on local binary pattern and twodimensional principal component analysis. To minimize illumination effects, the face image undergoes the local binary pattern operation, and the resultant image are divided into two subimages. Then, twodimensional principal component analysis algorithm is separately applied to each subimages. The individual scores obtained from two subimages are integrated using a weightedsummation rule, and the fusedscore is utilized to classify the unknown user. The performance evaluation of the proposed system was performed using the Yale B database and CMUPIE database, and the proposed method shows the better recognition results in comparison with existing face recognition techniques.