Towards Visual Saliency Explanations of Face Recognition

Deep convolutional neural networks have been pushing the frontier of face recognition (FR) techniques in the past years. Despite the high accuracy, they are often criticized for lacking explainability. There has been an increasing demand for understanding the decision-making process of deep face recognition systems. Recent studies have investigated using visual saliency maps as an explanation, but they often lack a discussion and analysis in the context of face recognition. This paper conceives a new explanation framework for face recognition. It starts by providing a new definition of the saliency-based explanation method, which focuses on the decisions made by the deep FR model. Then, a novel correlation-based RISE algorithm (CorrRISE) is proposed to produce saliency maps, which reveal both the similar and dissimilar regions of any given pair of face images. Besides, two evaluation metrics are designed to measure the performance of general visual saliency explanation methods in face recognition. Consequently, substantial visual and quantitative results have shown that the proposed method consistently outperforms other explainable face recognition approaches

DuetFace: Collaborative Privacy-Preserving Face Recognition via Channel Splitting in the Frequency Domain

With the wide application of face recognition systems, there is rising concern that original face images could be exposed to malicious intents and consequently cause personal privacy breaches. This paper presents DuetFace, a novel privacy-preserving face recognition method that employs collaborative inference in the frequency domain. Starting from a counterintuitive discovery that face recognition can achieve surprisingly good performance with only visually indistinguishable high-frequency channels, this method designs a credible split of frequency channels by their cruciality for visualization and operates the server-side model on non-crucial channels. However, the model degrades in its attention to facial features due to the missing visual information. To compensate, the method introduces a plug-in interactive block to allow attention transfer from the client-side by producing a feature mask. The mask is further refined by deriving and overlaying a facial region of interest (ROI). Extensive experiments on multiple datasets validate the effectiveness of the proposed method in protecting face images from undesired visual inspection, reconstruction, and identification while maintaining high task availability and performance. Results show that the proposed method achieves a comparable recognition accuracy and computation cost to the unprotected ArcFace and outperforms the state-of-the-art privacy-preserving methods. The source code is available at https://github.com/Tencent/TFace/tree/master/recognition/tasks/duetface.Comment: Accepted to ACM Multimedia 202

SynthDistill: Face Recognition with Knowledge Distillation from Synthetic Data

State-of-the-art face recognition networks are often computationally expensive and cannot be used for mobile applications. Training lightweight face recognition models also requires large identity-labeled datasets. Meanwhile, there are privacy and ethical concerns with collecting and using large face recognition datasets. While generating synthetic datasets for training face recognition models is an alternative option, it is challenging to generate synthetic data with sufficient intra-class variations. In addition, there is still a considerable gap between the performance of models trained on real and synthetic data. In this paper, we propose a new framework (named SynthDistill) to train lightweight face recognition models by distilling the knowledge of a pretrained teacher face recognition model using synthetic data. We use a pretrained face generator network to generate synthetic face images and use the synthesized images to learn a lightweight student network. We use synthetic face images without identity labels, mitigating the problems in the intra-class variation generation of synthetic datasets. Instead, we propose a novel dynamic sampling strategy from the intermediate latent space of the face generator network to include new variations of the challenging images while further exploring new face images in the training batch. The results on five different face recognition datasets demonstrate the superiority of our lightweight model compared to models trained on previous synthetic datasets, achieving a verification accuracy of 99.52% on the LFW dataset with a lightweight network. The results also show that our proposed framework significantly reduces the gap between training with real and synthetic data. The source code for replicating the experiments is publicly released.Comment: Accepted in the IEEE International Joint Conference on Biometrics (IJCB 2023

Person recognition based on deep gait: a survey.

Gait recognition, also known as walking pattern recognition, has expressed deep interest in the computer vision and biometrics community due to its potential to identify individuals from a distance. It has attracted increasing attention due to its potential applications and non-invasive nature. Since 2014, deep learning approaches have shown promising results in gait recognition by automatically extracting features. However, recognizing gait accurately is challenging due to the covariate factors, complexity and variability of environments, and human body representations. This paper provides a comprehensive overview of the advancements made in this field along with the challenges and limitations associated with deep learning methods. For that, it initially examines the various gait datasets used in the literature review and analyzes the performance of state-of-the-art techniques. After that, a taxonomy of deep learning methods is presented to characterize and organize the research landscape in this field. Furthermore, the taxonomy highlights the basic limitations of deep learning methods in the context of gait recognition. The paper is concluded by focusing on the present challenges and suggesting several research directions to improve the performance of gait recognition in the future

Os principais contributos da inteligência artificial para o processamento de imagens digitais a utilizar na segurança pública

Dissertação de mestrado em Segurança e Justiça, Universidade Lusíada de Lisboa, 2022Exame público realizado em 17 de Outubro de 2022Este estudo apresenta uma visão de necessidade evolucionista, da atuação das Forças e Serviços de Segurança, na comparticipação produzida por alguns dos novos avanços da Inteligência Artificial, que vem mudar de forma radical, a conceção clássica de acesso e salvaguarda de dados em formato digital, tendo em vista contribuir de forma direta para o aumento da assertividade na Investigação Criminal, com reflexo direto na melhoria da manutenção da Segurança Pública. A Inteligência Artificial, permite de forma autónoma, rastrear, identificar e informar com precisão, sobre pessoas, viaturas e diversos objetos num dado tempo e espaço, atuando como um aliado para potenciar a Segurança Pública, pela sua aplicação no processamento da análise digital dos registos de imagens, produzindo impacto na prevenção, auditoria criminal, concomitantemente no desempenho dos profissionais de segurança. Assim, afigura-se como relevante estudar o uso destes instrumentos, para permitir que os órgãos políticos, a sociedade civil e as Forças e Serviços de Segurança, melhorem, encontrem, meios tecnológicos, para cumprir com crescente facilidade e agilidade, todos os pressupostos de eficácia e eficiência na securitização pública, mas, também observar a preservação dos direitos, liberdades e garantias dos cidadãos.This study presents a vision of evolutionary need, the performance of the Security Forces and Services, in the participation produced by some of the new advances of Artificial Intelligence, which changes radically, the classic conception of access and safeguarding of data in digital format, with a view to contributing directly to the increase of assertiveness in Criminal Investigation, with a direct impact on improving the maintenance of Public Safety. The Artificial Intelligence, allows autonomously, to accurately track, identify and inform, about people, vehicles and various objects in a given time and space, acting as an ally to enhance Public Security, for its application in the processing of digital analysis of image records, producing impact on prevention, criminal audit, concomitantly in the performance of security professionals. Thus, it seems relevant to study the use of these instruments, to allow political bodies, civil society and security forces and services to improve, find technological means, to comply with increasing ease and agility, all the assumptions of effectiveness and efficiency in public securitization, but also to observe the preservation of citizens' rights, freedoms and guarantees