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

Improving Face Sketch Recognition via Adversarial Sketch-Photo Transformation

International audiencefeature learning [7]-[10]. The benefit of the former category relates to the conversion of sketches into the same modality as photos, and hence lies in the ability to utilize existing photo-based face recognition methods. Thus, the applicability of the existing photo-based face recognition algorithms can be greatly expanded. Current methods for face photo-sketch transformation can be mainly grouped into example-based methods and regression-based methods. Example-based methods assume that the corresponding sketches (or patches of sketches) of two similar face photos (or patches of face photos) are also similar. Such methods rely on face photo-sketch pairs in the training set to synthesize images. In order to achieve good transformation results, these methods usually require a large number of photo-sketch pairs. However, the computational cost may also grow linearly with the increase of the training set size. Regression-based methods overcome the issues mentioned above and the most time-consuming part only exists in the training stage when learning the mapping between face photos and sketches, but the inference/testing stage can be fast. In this paper, we propose a Generative Adversarial Network (GAN) for face sketch-to-photo transformation , leveraging the advantages of CycleGAN [11] and conditional GANs [12]. We have designed a new feature-level loss, which is jointly used with the traditional image-level adversarial loss to ensure the quality of the synthesized photos. The proposed approach outperforms state-of-the-art approaches for synthesizing photos in terms of structural similarity index (SSIM). More importantly, the synthesized photos of our approach are found to be more instrumental in improving the sketch-to-photo matching accuracy. The rest of this paper is organized as follows: Section II summarizes representative methods of face photo-to-sketch transformation, and GANs. Section III provides details of the proposed method and the designed feature-level loss. Experimental results and analysis are presented in Section IV. Finally, we conclude this work in Section V. Abstract-Face sketch-photo transformation has broad applications in forensics, law enforcement, and digital entertainment, particular for face recognition systems that are designed for photo-to-photo matching. While there are a number of methods for face photo-to-sketch transformation, studies on sketch-to-photo transformation remain limited. In this paper, we propose a novel conditional CycleGAN for face sketch-to-photo transformation. Specifically, we leverage the advantages of CycleGAN and conditional GANs and design a feature-level loss to assure the high quality of the generated face photos from sketches. The generated face photos are used, as a replacement of face sketches, and particularly for face identification against a gallery set of mugshot photos. Experimental results on the public-domain database CUFSF show that the proposed approach is able to generate realistic photos from sketches, and the generated photos are instrumental in improving the sketch identification accuracy against a large gallery set