Face Retrieval Based On Local Binary Pattern and Its Variants: A Comprehensive Study
Phan Khoi, Lam Huu, Vo Hoai · International Journal of Advanced Computer Science and Applications · 2016
Face retrieval (FR) is one of the specific fields in content-based image retrieval (CBIR). Its aim is to search relevant faces in large database based on the contents of the images rather than the metadata. It has many applications in important areas such as face searching, forensics, and identification… In this paper, we experimentally evaluate Face Retrieval based on Local Binary Pattern (LBP) and its variants: Rotation Invariant Local Binary Pattern (RILBP) and Pyramid of Local Binary Pattern (PLBP). We also use a grid LBP based operator, which divides an image into 6×7 sub-regions then concentrates LBP feature vector from each of them into a spatially enhanced feature histogram. These features were firstly tested on three fontal face datasets: The Database of Faces (TDF), Caltech Faces 1999 (CF1999) and the combination of The Database of faces and Caltech Faces 1999 (CF). Good result on these dataset has encouraged us to conduct tests on Labeled Faces in the Wild (LFW), where the images were taken from real-world condition. Mean average precision (MAP) was used for measuring the performance of the system. We carry out the experiments in two main stages indexing and searching with the use of k-fold cross-validation. We further boost the system by using Locality Sensitive Hashing (LSH). Furthermore, we also evaluate the impact of LSH on the searching stage. The experimental results have shown that LSH is effective for face searching as well as LBP is robust feature in fontal face retrieval