Iurii Medvedev

Jan Niklas Kolf, Fadi Boutros, Jurek Elliesen et al.

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition benchmarks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines. The submitted solutions use small, efficient network architectures to reduce the computational cost, some solutions apply model quantization. An outlook on possible techniques that are underrepresented in current solutions is given as well.

Guilherme Schardong, Tiago Novello, Hallison Paz et al.

Face morphing is a problem in computer graphics with numerous artistic and forensic applications. It is challenging due to variations in pose, lighting, gender, and ethnicity. This task consists of a warping for feature alignment and a blending for a seamless transition between the warped images. We propose to leverage coord-based neural networks to represent such warpings and blendings of face images. During training, we exploit the smoothness and flexibility of such networks by combining energy functionals employed in classical approaches without discretizations. Additionally, our method is time-dependent, allowing a continuous warping/blending of the images. During morphing inference, we need both direct and inverse transformations of the time-dependent warping. The first (second) is responsible for warping the target (source) image into the source (target) image. Our neural warping stores those maps in a single network dismissing the need for inverting them. The results of our experiments indicate that our method is competitive with both classical and generative models under the lens of image quality and face-morphing detectors. Aesthetically, the resulting images present a seamless blending of diverse faces not yet usual in the literature.

Iurii Medvedev, Farhad Shadmand, Nuno Gonçalves

Face morphing attack detection (MAD) is one of the most challenging tasks in the field of face recognition nowadays. In this work, we introduce a novel deep learning strategy for a single image face morphing detection, which implies the discrimination of morphed face images along with a sophisticated face recognition task in a complex classification scheme. It is directed onto learning the deep facial features, which carry information about the authenticity of these features. Our work also introduces several additional contributions: the public and easy-to-use face morphing detection benchmark and the results of our wild datasets filtering strategy. Our method, which we call MorDeephy, achieved the state of the art performance and demonstrated a prominent ability for generalising the task of morphing detection to unseen scenarios.

Iurii Medvedev, Farhad Shadmand, Nuno Gonçalves

Face recognition has achieved outstanding performance in the last decade with the development of deep learning techniques. Nowadays, the challenges in face recognition are related to specific scenarios, for instance, the performance under diverse image quality, the robustness for aging and edge cases of person age (children and elders), distinguishing of related identities. In this set of problems, recognizing children's faces is one of the most sensitive and important. One of the reasons for this problem is the existing bias towards adults in existing face datasets. In this work, we present a benchmark dataset for children's face recognition, which is compiled similarly to the famous face recognition benchmarks LFW, CALFW, CPLFW, XQLFW and AgeDB. We also present a development dataset (separated into train and test parts) for adapting face recognition models for face images of children. The proposed data is balanced for African, Asian, Caucasian, and Indian races. To the best of our knowledge, this is the first standartized data tool set for benchmarking and the largest collection for development for children's face recognition. Several face recognition experiments are presented to demonstrate the performance of the proposed data tool set.

Iurii Medvedev, Nuno Gonçalves

Recent advancements in deep learning have revolutionized technology and security measures, necessitating robust identification methods. Biometric approaches, leveraging personalized characteristics, offer a promising solution. However, Face Recognition Systems are vulnerable to sophisticated attacks, notably face morphing techniques, enabling the creation of fraudulent documents. In this study, we introduce a novel quadruplet loss function for increasing the robustness of face recognition systems against morphing attacks. Our approach involves specific sampling of face image quadruplets, combined with face morphs, for network training. Experimental results demonstrate the efficiency of our strategy in improving the robustness of face recognition networks against morphing attacks.

Iurii Medvedev, Nuno Goncalves

Face recognition has evolved significantly with the advancement of deep learning techniques, enabling its widespread adoption in various applications requiring secure authentication. However, this progress has also increased its exposure to presentation attacks, including face morphing, which poses a serious security threat by allowing one identity to impersonate another. Therefore, modern face recognition systems must be robust against such attacks. In this work, we propose a novel approach for training deep networks for face recognition with enhanced robustness to face morphing attacks. Our method modifies the classification task by introducing a dual-branch classification strategy that effectively handles the ambiguity in the labeling of face morphs. This adaptation allows the model to incorporate morph images into the training process, improving its ability to distinguish them from bona fide samples. Our strategy has been validated on public benchmarks, demonstrating its effectiveness in enhancing robustness against face morphing attacks. Furthermore, our approach is universally applicable and can be integrated into existing face recognition training pipelines to improve classification-based recognition methods.

Iurii Medvedev, Joana Pimenta, Nuno Gonçalves

Face morphing, a sophisticated presentation attack technique, poses significant security risks to face recognition systems. Traditional methods struggle to detect morphing attacks, which involve blending multiple face images to create a synthetic image that can match different individuals. In this paper, we focus on the differential detection of face morphing and propose an extended approach based on fused classification method for no-reference scenario. We introduce a public face morphing detection benchmark for the differential scenario and utilize a specific data mining technique to enhance the performance of our approach. Experimental results demonstrate the effectiveness of our method in detecting morphing attacks.

Joana Pimenta, Iurii Medvedev, Nuno Gonçalves

The increase in security concerns due to technological advancements has led to the popularity of biometric approaches that utilize physiological or behavioral characteristics for enhanced recognition. Face recognition systems (FRSs) have become prevalent, but they are still vulnerable to image manipulation techniques such as face morphing attacks. This study investigates the impact of the alignment settings of input images on deep learning face morphing detection performance. We analyze the interconnections between the face contour and image context and suggest optimal alignment conditions for face morphing detection.