Literature Study of Face Recognition using The Viola-Jones Algorithm

The face is the front part of a human expression comprising the eyes, nose, lips, cheeks, forehead, and chin. These characters are uniquely placed according to a human's pattern of face. The Viola-Jones algorithm is used to recognize and detect objects, including this human face. It consists of several stages, such as Haar-like Filter, Integral Image, Adaboost Algorithm, and Cascade. Haar-Like filter is used to determine feature values from images containing certain objects. Furthermore, integral image helps to find the feature value to quicken the calculation process. Adaboost algorithm processes feature selection by determining the threshold value in order to determine the existing object. Meanwhile, cascade performs an image selection process that contains or excludes objects with large amounts of test data. It directly discards the figure when no objects are detected to produce images containing objects. This study is a literature review on facial recognition using the Viola-Jones algorithm. It contributes to the search for the suitability of using the Viola-Jones Algorithm in certain cases. The research contribution also lies in the researcher's idea for future research, namely testing the Viola-Jones algorithm in recognizing objects other than facial images. Furthermore, five studies are analyzed and described the application of the Viola-Jones algorithm for facial recognition with their respective advantages. The first study had a very good accuracy level of 85%-95% in detecting faces. The second study had accuracy, precision, recall, and achievement times of 0.74, 0.73, 0.76, and 15 seconds in recognizing a person's emotions through facial expressions. Meanwhile, the third study had a very good accuracy level of 94.5% in recognizing faces that are 1 meter away.