Example image-based feature extraction for face recognition

This paper proposes a novel method for recognizing facial images based on the relative distances between an input image and example images. Example facial images can be easily collected online, and a large example database can span new possible facial variations not sufficiently learned during the learning phase. We first extract facial features using a baseline classifier that has a certain degree of accuracy. To achieve a better performance of the proposed method, we divide the collected examples into groups using a clustering method (e.g., k-means), where each clustered group contains examples with similar characteristics. We then hierarchically partition a group formed in the previous level into other groups to analyze more specific facial characteristics, which represent an example pyramid. To describe the characteristics of a group using the clustered examples, we divide the example group into a number of sub-groups. We calculate the averages of the sub-groups and select an example most similar to the average in each sub-group because we assume that the averages of the sub-groups can directly represent their characteristics. Using the selected examples, we build example code words for a novel feature extraction. The example code words are used to measure the distances to an input image and serve as anchors to analyze a facial image in the example domain. The distance values are normalized for each group at all pyramid levels, and are concatenated to form novel features for face recognition. We verified the effectiveness of the proposed example pyramid framework using well-known proposed features, including LBP, HOG, Gabor, and the deep learning method, on the LFW database, and showed that it can yield significant improvements in recognition performance.