Face Retrieval using Combination of Gradient Histogram and Local Binary Pattern

Face retrieval is an important research topic in image processing and aims finding face images similar to a query image. In this paper, a novel method is proposed to retrieve face images using gradient histogram and local binary pattern (LBP). The combination of these two techniques will increase the robustness against face variations and thus improve system performance in face retrieval. In order to increase system ability, a relevance feedback scheme based on support vector machine (SVM) is proposed. The Experiments have been conducted on the AR face database in two modes: without occluded images and with occluded images. Experimental results show that the proposed method can retrieve face images effectively. In the next, the proposed method is compared with several successful methods in face researches. Mean average precision (MAP) metric for the proposed method in two experimental modes is equal to 94.40% and 68.12% , while the best results for compared methods is 90.37% and 61.99%, respectively. The results show that the proposed method is superior to these methods and is a good method to retrieve the face images.