An improved approach towards biometric face recognition using artificial neural network

In a biometric-based security system, rather than depending on the system configuration itself, failure rate also relies upon feature extraction and its related statistics. In this paper, a significant approach is being presented to minimize the failure rate and maintain high recognition accuracy and uniformity for non-symmetrical feature points. This work contributes a detailed analysis of stable parameters of captured biometric feature points by using a flexible learning model named as adopted Artificial Neural Network (ANN). The paper also discusses a comparative study of different global and local methods of Histogram of an Equivalent Pattern (HEP) technique for facial feature detection and extraction. The HEP, is further classified by using adopted ANN model, which depends on partitioning the feature area of a predefined image. This task has been accomplished by providing the appropriate definition of local and global functions based on pixel intensities. The literature available for face detection shows many shortcomings such as false acceptance and rejection rates. Among all defined global and local techniques, this paper primarily endorsed an adopted method of Improved Local Binary Pattern (ILBP) which works on local pixel values of a facial image for feature extraction. The classification and recognition task are performed by adopted ANN for various defined global and local features. The paper also derives a detailed comparison with the other existing techniques. As a result, the proposed ILBP technique ensures the consistency of acceptable results in unpredictable variations in the dataset.