The Impact of Preprocessing on Face Recognition Using Pseudorandom Pixel Placement

The condition of face images, data processing algorithms, and hardware capabilities can influence the accuracy of face recognition. Several studies have been conducted to increase performance of face recognition. One of the steps is to create or even improve the methods in preprocessing as one of the essential steps that can affect accuracy. This paper proposed a pseudorandom pixel placement method applied to the preprocessing step in face recognition to know the impact on accuracy. Eight face objects were used in this study. One face image for one object as training data was taken via a single-lens digital reflex camera and a smartphone. One video for one object was taken from Closed Circuit Television with two different placement angle conditions for testing data. The experiment was carried out with four variations of the basic resolution size of the face image in the testing data to see the performance of the proposed method. The result is five of eight face objects have improved accuracy than without using pseudorandom pixel placement. The best average accuracy result using the proposed method is 63.76% higher than without using the proposed method with a value of 60.09%, so preprocessing using the pseudorandom pixel placement on face recognition can increase accuracy.