Abstract
Face representation and matching are two essential issues in face verification task. Various approaches have been proposed focusing on these two issues. However, few of them addressed the joint optimal solutions of these two issues in a unified framework. In this paper, we present a second-order face representation method for face pair and a unified face verification framework, in which the feature extractors and the subsequent binary classification model design can be selected flexibly. Our contributions can be summarized in the following aspects. First, a novel face-pair representation method that employs the second-order statistical property of the face pairs is proposed, which retains more information compared to the existing methods. Second, a flexible binary classification model, which differs from the conventionally used metric learning, is constructed based on the new face-pair representation. Finally, we verify that our proposed face-pair representation can benefit from large training datasets. All the experiments are carried out on Labeled Face in the Wild (LFW) to verify the algorithm’s effectiveness against challenging uncontrolled conditions.
Highlights
Face recognition has been extensively studied in the field of computer vision and pattern recognition, which in general can be categorized into two tasks, that is, face verification and face identification
We firstly present a second-order face-pair representation, and the task of face verification can be viewed as a binary classification problem in a unified framework
We propose a unified face verification framework, in which the face-pair representation is employed instead of a single-face representation
Summary
Face recognition has been extensively studied in the field of computer vision and pattern recognition, which in general can be categorized into two tasks, that is, face verification and face identification. Like most pattern recognition systems, face verification task has two key components, that is, face representation and face matching. Harr-like features [3] are rule-based local feature descriptors Such handcrafted encoding methods are suspected to get optimal encoding for a specific task. Mahalanobis-like distance learning can be viewed as an Euclidean distance in the linearly transformed feature space This refers to a more extensively researched topic, that is, kinds of projection techniques, which we have described in the above section as a feature representation approach.
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