Deep Feature Representation and Similarity Matrix based Noise Label Refinement Method for Efficient Face Annotation

Face annotation is a naming procedure that assigns the correct name to a person emerging from an image. Faces that are manually annotated by people in online applications include incorrect labels, giving rise to the issue of label ambiguity. This may lead to mislabelling in face annotation. Consequently, an efficient method is still essential to enhance the reliability of face annotation. Hence, in this work, a novel method named the Similarity Matrix-based Noise Label Refinement (SMNLR) is proposed, which effectively predicts the accurate label from the noisy labelled facial images. To enhance the performance of the proposed method, the deep learning technique named Convolutional Neural Networks (CNN) is used for feature representation. Several experiments are conducted to evaluate the effectiveness of the proposed face annotation method using the LFW, IMFDB and Yahoo datasets. The experimental results clearly illustrate the robustness of the proposed SMNLR method in dealing with noisy labelled faces

Automatic face image annotation based on a single template with constrained warping deformation

In this study, an automatic face image annotation method is proposed by aligning faces with different expressions to an annotated neutral face. This work is useful in reducing tedious manual work for labelling image data in large databases. However, it is challenging because of the appearance variations caused by non‐rigid face deformations under various expressions. Unlike some conventional approaches acquiring sufficient image templates to model the query appearance, only a single given template is necessary for the proposed method. The authors address the problem through dense image alignment. Specifically, image warping in the alignment process is constrained by prior knowledge about facial shape deformation. The proposed method is independent of the appearance model, and is available for unseen faces. In addition, to initialise warping parameters, the authors present a robust patch‐based estimation method. Context information for feature points is carefully modelled to propagate the searching path for local patch matching. The face annotation experiments are performed on some large expressions, with noisy image qualities and in low image resolutions. Comparison results with conventional methods demonstrate the proposed method's superiority on both accuracy and robustness