Privacy-Preserving Face Recognition Using Random Frequency Components

The ubiquitous use of face recognition has sparked increasing privacy concerns, as unauthorized access to sensitive face images could compromise the information of individuals. This paper presents an in-depth study of the privacy protection of face images' visual information and against recovery. Drawing on the perceptual disparity between humans and models, we propose to conceal visual information by pruning human-perceivable low-frequency components. For impeding recovery, we first elucidate the seeming paradox between reducing model-exploitable information and retaining high recognition accuracy. Based on recent theoretical insights and our observation on model attention, we propose a solution to the dilemma, by advocating for the training and inference of recognition models on randomly selected frequency components. We distill our findings into a novel privacy-preserving face recognition method, PartialFace. Extensive experiments demonstrate that PartialFace effectively balances privacy protection goals and recognition accuracy. Code is available at: https://github.com/Tencent/TFace.Comment: ICCV 202

Privacy-Preserving Face Recognition with Learnable Privacy Budgets in Frequency Domain

Face recognition technology has been used in many fields due to its high recognition accuracy, including the face unlocking of mobile devices, community access control systems, and city surveillance. As the current high accuracy is guaranteed by very deep network structures, facial images often need to be transmitted to third-party servers with high computational power for inference. However, facial images visually reveal the user's identity information. In this process, both untrusted service providers and malicious users can significantly increase the risk of a personal privacy breach. Current privacy-preserving approaches to face recognition are often accompanied by many side effects, such as a significant increase in inference time or a noticeable decrease in recognition accuracy. This paper proposes a privacy-preserving face recognition method using differential privacy in the frequency domain. Due to the utilization of differential privacy, it offers a guarantee of privacy in theory. Meanwhile, the loss of accuracy is very slight. This method first converts the original image to the frequency domain and removes the direct component termed DC. Then a privacy budget allocation method can be learned based on the loss of the back-end face recognition network within the differential privacy framework. Finally, it adds the corresponding noise to the frequency domain features. Our method performs very well with several classical face recognition test sets according to the extensive experiments.Comment: ECCV 2022; Code is available at https://github.com/Tencent/TFace/tree/master/recognition/tasks/dctd

Cellular image classification

This book introduces new techniques for cellular image feature extraction, pattern recognition and classification. The authors use the antinuclear antibodies (ANAs) in patient serum as the subjects and the Indirect Immunofluorescence (IIF) technique as the imaging protocol to illustrate the applications of the described methods. Throughout the book, the authors provide evaluations for the proposed methods on two publicly available human epithelial (HEp-2) cell datasets: ICPR2012 dataset from the ICPR'12 HEp-2 cell classification contest and ICIP2013 training dataset from the ICIP'13 Competition on cells classification by fluorescent image analysis. First, the reading of imaging results is significantly influenced by one’s qualification and reading systems, causing high intra- and inter-laboratory variance. The authors present a low-order LP21 fiber mode for optical single cell manipulation and imaging staining patterns of HEp-2 cells. A focused four-lobed mode distribution is stable and effective in optical tweezer applications, including selective cell pick-up, pairing, grouping or separation, as well as rotation of cell dimers and clusters. Both translational dragging force and rotational torque in the experiments are in good accordance with the theoretical model. With a simple all-fiber configuration, and low peak irradiation to targeted cells, instrumentation of this optical chuck technology will provide a powerful tool in the ANA-IIF laboratories. Chapters focus on the optical, mechanical and computing systems for the clinical trials. Computer programs for GUI and control of the optical tweezers are also discussed. to more discriminative local distance vector by searching for local neighbors of the local feature in the class-specific manifolds. Encoding and pooling the local distance vectors leads to salient image representation. Combined with the traditional coding methods, this method achieves higher classification accuracy. Then, a rotation invariant textural feature of Pairwise Local Ternary Patterns with Spatial Rotation Invariant (PLTP-SRI) is examined. It is invariant to image rotations, meanwhile it is robust to noise and weak illumination. By adding spatial pyramid structure, this method captures spatial layout information. While the proposed PLTP-SRI feature extracts local feature, the BoW framework builds a global image representation. It is reasonable to combine them together to achieve impressive classification performance, as the combined feature takes the advantages of the two kinds of features in different aspects. Finally, the authors design a Co-occurrence Differential Texton (CoDT) feature to represent the local image patches of HEp-2 cells. The CoDT feature reduces the information loss by ignoring the quantization while it utilizes the spatial relations among the differential micro-texton feature. Thus it can increase the discriminative power. A generative model adaptively characterizes the CoDT feature space of the training data. Furthermore, exploiting a discriminant representation allows for HEp-2 cell images based on the adaptive partitioned feature space. Therefore, the resulting representation is adapted to the classification task. By cooperating with linear Support Vector Machine (SVM) classifier, this framework can exploit the advantages of both generative and discriminative approaches for cellular image classification. The book is written for those researchers who would like to develop their own programs, and the working MatLab codes are included for all the important algorithms presented. It can also be used as a reference book for graduate students and senior undergraduates in the area of biomedical imaging, image feature extraction, pattern recognition and classification. Academics, researchers, and professional will find this to be an exceptional resource

Real-time measurement of nano-particle size using differential optical phase detection

We demonstrate a size sensing technique for nano-particles using optical differential phase measurement by a dual fiber interferometer through phase-generated carrier (PGC) demodulation. Nano-particle diameters are obtained from the differential phase shift as a result of adding an optical scattering perturbation into two-beam interference. Polystyrene nano-particles with diameters from 200 to 900 nm in a microfluidic channel are detected using this technique to acquire real-time particle diameters. Compared with amplitude sensing with over 10 mW of laser irradiance, particle sizing by PGC phase sensing can be achieved at a laser power as low as 1.18 mW. We further analyze major sources of noise in order to improve the limits of detection. This sensing technique may find a broad range of applications from the real-time selection of biological cell samples to rare cell detection in blood samples for early cancer screening.Published versio

Formation of Deposition Patterns Induced by the Evaporation of the Restricted Liquid

Evaporation-induced self-assembly of colloids or suspensions has received increasing attention. Given its critical applications in many fields of science and industry, we report deposition patterns constructed by the evaporation of the restricted aqueous suspension with polystyrene particles at different substrate temperatures and geometric container dimensions. With the temperature increases, the deposition patterns transition from honeycomb to multiring to island, which is attributed to the competition between the particle deposition rate U-p and the contact line velocity U-CL, and the dimension of the geometric container has an effect on the characteristics of patterns. In this paper, the formation of an ordered multiring pattern is mainly focused on as a result of U-p keeping up with U-CL such that the entire contact line can be pinned, that is, the periodic stick-slip motion of the contact line and the particle sedimentation. Moreover, based on the Onsager principle, we develop a theoretical model to reveal the physical mechanisms behind the multiring phenomena. The position and spacing of rings are measured, which shows that the theoretical prediction agrees well with experiments. We also find that the ring spacing decays exponentially from center to edge experimentally and theoretically. This may not only help us to understand the formation of the deposition patterns but also assist future design and control in practical applications. Evaporation-induced self-assembly of colloids or suspensions has received increasing attention. Given its critical applications in many fields of science and industry, we report deposition patterns constructed by the evaporation of the restricted aqueous suspension with polystyrene particles at different substrate temperatures and geometric container dimensions. With the temperature increases, the deposition patterns transition from honeycomb to multiring to island, which is attributed to the competition between the particle deposition rate U-P and the contact line velocity U-CL, and the dimension of the geometric container has an effect on the characteristics of patterns. In this paper, the formation of an ordered multiring pattern is mainly focused on as a result of U-P keeping up with U-CL such that the entire contact line can be pinned, that is, the periodic stick-slip motion of the contact line and the particle sedimentation. Moreover, based on the Onsager principle, we develop a theoretical model to reveal the physical mechanisms behind the multiring phenomena. The position and spacing of rings are measured, which shows that the theoretical prediction agrees well with experiments. We also find that the ring spacing decays exponentially from center to edge experimentally and theoretically. This may not only help us to understand the formation of the deposition patterns but also assist future design and control in practical applications

Optimum O2:CH4 Ratio Promotes the Synergy between Aerobic Methanotrophs and Denitrifiers to Enhance Nitrogen Removal

The O2:CH4 ratio significantly effects nitrogen removal in mixed cultures where aerobic methane oxidation is coupled with denitrification (AME-D). The goal of this study was to investigate nitrogen removal of the AME-D process at four different O2:CH4 ratios [0, 0.05, 0.25, and 1 (v/v)]. In batch tests, the highest denitrifying activity was observed when the O2:CH4 ratio was 0.25. At this ratio, the methanotrophs produced sufficient carbon sources for denitrifiers and the oxygen level did not inhibit nitrite removal. The results indicated that the synergy between methanotrophs and denitrifiers was significantly improved, thereby achieving a greater capacity of nitrogen removal. Based on thermodynamic and chemical analyses, methanol, butyrate, and formaldehyde could be the main trophic links of AME-D process in our study. Our research provides valuable information for improving the practical application of the AME-D systems