Face identification using reference-based features with message passing model

Abstract

In this paper, we propose a system for face identification. Given two query face images, our task is to tell whether or not they are of the same person. The main contribution of this paper comes from two aspects: (1) We adopt the one-shot similarity kernel [35] for learning the similarity of two face images. The learned similarity measures are then used to map a face image to reference images. (2) We propose a graph-based method for selecting an optimal set of reference images. Instead of directly working on the image features, we use the learned similarity to the reference images as the new features and compute the corresponding matching score of the two query images. Our approach is effective and easy to implement. We show encouraging and favorable results on the "Labeled Faces in the Wild" -a challenging data set of faces.

Reference Face Graph for Face Recognition

“…learning [51], and message passing model [25] using Receiver Operating Characteristic (ROC) curve for face verification on the LFW database. For these methods same LBP features are used as compared to the features in our approach.…”

Collaborative similarity metric learning for face recognition in the wild

“…Many studies such as [2–6] investigated obtaining better representations and descriptors to facilitate face verification for given dissimilarity measures. Many other studies aimed at learning a better discriminative distance metric [7–10], while some investigated learning both distance metric and image representations together [11–13]. Most of the DML methods focus mainly on learning a linear/nonlinear function of image representations, so that the desired dissimilarity cost is minimised when two (or more) images are inputted to this function.…”

Face Recognition Using Dense SIFT Feature Alignment

“…Automatic face recognition, as a high level vision task, is designed to distinguish a specific identity from the unknown objects characterized by facial images. It has been extensively studied in computer vision [1−7] , and suffices various practical applications, such as face identification [8] , biometrics [9] , and video surveillance [10] .…”