Facial Peculiarity Retrieval via Deep Neural Networks Fusion

Abstract

Face retrieval is becoming increasingly useful and important for security maintenance operations. In actual applications, face retrieval is usually influenced by some changeable site conditions, such as various postures, expressions, camera angles, illuminations, and so on. In this paper, facial peculiar features are extracted and classified by dynamically integrated deep neural networks (DNNs), in order to enhance the adaptability in actual conditions. Firstly, eight kinds of facial components are detected and located by clustering analysis and Active Shape Model (ASM). Secondly, certain peculiar patterns are defined for each kind of facial component, and eight specialized DNNs are designed to extract features and classify components. Thirdly, the similarity between faces is calculated by dynamically integrating the results of each DNN. Comparative experiments on standard image sets and wild image sets demonstrate that our algorithm outperforms global feature models in retrieval accuracy. Our algorithm is particularly suitable for practical application with regard to natural real videos and images.