The development of technologies such as employment tracking systems, personal security, and the use of robots has led a lot of studies on face recognition systems. In the most of studies considering face recognition, recognition accuracies are very high, since training and testing images are selected randomly from the same databases. However, in real life applications, these images are not randomly selected from the same database. Because, these systems are trained during installation of the recognition system or when a new person needs to be introduced to the system. On the other hand, images used for predictions are uploaded to the system at other times. In this study, it is aimed to show that the accuracy rates of real-life face recognition systems differ from the systems trained and tested with randomly selected images as usually done in literature. To observe this difference in the first step, training and test images are selected randomly. In the second step, training and test images are divided according to the recording dates as in real life. Accuracy rates are evaluated by using linear discriminant analysis, local binary patterns and principal component analysis methods. Although the accuracies are very high for the first step, it is seen that the accuracies fell dramatically in the second step for all methods. Afterwards a new method is searched also in this study to increase these low accuracy rates. It is shown that usage of eye area images instead of face images has higher accuracy rates in all above methods for real life applications.
Eye Area Recognition, OpenCv, Image Processing, Linear Discriminant Analysis, Local Binary Patterns, Principal Component Analysis, Face Recognition