Bag of Facial Components: A Data Enrichment Approach for Face Image Processing

Facial images are one of the raw data that can be processed to produce various information/representations, especially for computer vision, pattern recognition, and biometrics. Moreover, identity recognition, expression recognition and visitor demographic calculations are applications that can be generated through the processing of facial images. In order to perform face image processing, a face detection mechanism is needed to isolate the face area (region-of-interest-ROI). Previous research generally views facial images as unity for further processing with feature extraction techniques and recognition. This paper proposes post-processing from face detection (Viola-Jones based) to produce a bag of facial components as a data representation for the next processes which are feature extraction and recognition. The post-processing is done based on the geometric rules of the face and golden ratio to produce more accurate detection. From the experiment, the proposed method achieves 96.88% of accuracy on the development part whilst the accuracy of testing part reaches 92.52% (with precision 95.32% and recall 96.62%).