Advanced Techniques for Face Recognition under Challenging Environments

Automatically recognizing faces captured under uncontrolled environments has always been a challenging topic in the past decades. In this work, we investigate cohort score normalization that has been widely used in biometric verification as means to improve the robustness of face recognition under challenging environments. In particular, we introduce cohort score normalization into undersampled face recognition problem. Further, we develop an effective cohort normalization method specifically for the unconstrained face pair matching problem. Extensive experiments conducted on several well known face databases demonstrate the effectiveness of cohort normalization on these challenging scenarios. In addition, to give a proper understanding of cohort behavior, we study the impact of the number and quality of cohort samples on the normalization performance. The experimental results show that bigger cohort set size gives more stable and often better results to a point before the performance saturates. And cohort samples with different quality indeed produce different cohort normalization performance. Recognizing faces gone after alterations is another challenging problem for current face recognition algorithms. Face image alterations can be roughly classified into two categories: unintentional (e.g., geometrics transformations introduced by the acquisition devide) and intentional alterations (e.g., plastic surgery). We study the impact of these alterations on face recognition accuracy. Our results show that state-of-the-art algorithms are able to overcome limited digital alterations but are sensitive to more relevant modifications. Further, we develop two useful descriptors for detecting those alterations which can significantly affect the recognition performance. In the end, we propose to use the Structural Similarity (SSIM) quality map to detect and model variations due to plastic surgeries. Extensive experiments conducted on a plastic surgery face database demonstrate the potential of SSIM map for matching face images after surgeries

Boosting Few-shot Fine-grained Recognition with Background Suppression and Foreground Alignment

Few-shot fine-grained recognition (FS-FGR) aims to recognize novel fine-grained categories with the help of limited available samples. Undoubtedly, this task inherits the main challenges from both few-shot learning and fine-grained recognition. First, the lack of labeled samples makes the learned model easy to overfit. Second, it also suffers from high intra-class variance and low inter-class difference in the datasets. To address this challenging task, we propose a two-stage background suppression and foreground alignment framework, which is composed of a background activation suppression (BAS) module, a foreground object alignment (FOA) module, and a local to local (L2L) similarity metric. Specifically, the BAS is introduced to generate a foreground mask for localization to weaken background disturbance and enhance dominative foreground objects. What's more, considering the lack of labeled samples, we compute the pairwise similarity of feature maps using both the raw image and the refined image. The FOA then reconstructs the feature map of each support sample according to its correction to the query ones, which addresses the problem of misalignment between support-query image pairs. To enable the proposed method to have the ability to capture subtle differences in confused samples, we present a novel L2L similarity metric to further measure the local similarity between a pair of aligned spatial features in the embedding space. Extensive experiments conducted on multiple popular fine-grained benchmarks demonstrate that our method outperforms the existing state-of-the-art by a large margin.Comment: Preprint under review in TCSVT Journa

Human gait identification from extremely low-quality videos : an enhanced classifier ensemble method

Nowadays, surveillance cameras are widely installed in public places for security and law enforcement, but the video quality may be low because of the limited transmission bandwidth and storage capacity. In this study, the authors proposed a gait recognition method for extremely low-quality videos, which have a frame-rate at one frame per second (1 fps) and resolution of 32 × 22 pixels. Different from popular temporal reconstruction-based methods, the proposed method uses the average gait image (AGI) over the whole sequence as the appearance-based feature description. Based on the AGI description, the authors employed a large number of weak classifiers to reduce the generalisation errors. The performance can be further improved by incorporating the model-based information into the classifier ensemble. The authors found that the performance improvement is directly proportional to the average disagreement level of weak classifiers (i.e. diversity), which can be increased by using the model-based information. The authors evaluated the proposed method on both indoor and outdoor databases (i.e. the low-quality versions of OU-ISIR-D and USF databases), and the results suggest that our method is more general and effective than other state-of-the-art algorithms

Cohort normalization based sparse representation for undersampled face recognition

Abstract. In recent years, sparse representation based classification(SRC) has received much attention in face recognition with multipletraining samples of each subject. However, it cannot be easily applied toa recognition task with insufficient training samples under uncontrolledenvironments. On the other hand, cohort normalization, as a way of mea-suring the degradation effect under challenging environments in relationto a pool of cohort samples, has been widely used in the area of biometricauthentication. In this paper, for the first time, we introduce cohort nor-malization to SRC-based face recognition with insufficient training sam-ples. Specifically, a user-specific cohort set is selected to normalize theraw residual, which is obtained from comparing the test sample with itssparse representations corresponding to the gallery subject, using poly-nomial regression. Experimental results on AR and FERET databases show that cohort normalization can bring SRC much robustness against various forms of degradation factors for undersampled face recognition

Structural similarity based image quality map for face recognition across plastic surgery

Variations in the face appearance caused by plastic surgery on skin texture and geometric structure, can impair the performance of most current face recognition systems. In this work, we proposed to use the Structural Similarity (SSIM) quality map to detect and model variations due to plastic surgeries. In the proposed framework, a S-SIM index weighted multi-patch fusion scheme is developed, where different weights are provided to different patches in accordance with the degree to which each patch may be altered by surgeries. An important feature of the proposed approach, also achieving performance comparable with the current state-of-the-art, is that neither training process is needed nor any background information from other datasets is required. Extensive experiments conducted on a plastic surgery face database demonstrate the potential of SSIM map for matching face images after surgeries

Static and Dynamic Networking of Smart Meters Based on the Characteristics of the Electricity Usage Information

The normal communication between smart meter and concentrator is a key factor influencing the normal function of users’ power consumption systems. To solve the communication failure of the smart meter caused by the signal conflict as well as the collected consecutive information abnormality from the same smart meter, according to the chain optimization index, the networking method of static and dynamic combination proposed in this paper is first used to picked out the optimal relay for a smart meter belonging to multiple relay communication ranges. Meanwhile, the communication with other secondary relays is closed to avoid signal conflict. Then the paper forms different combinations of collected data and these combinations are trained in the extreme learning machine (ELM) to find the characteristics value of power consumption information. Finally, in MATLAB simulation, if ELM detects the abnormal information, new communication path could be promptly found through dynamic adjustment of chain optimization weighted coefficient and the weighted coefficient of the number of the relayed smart meters. It solves the problem of consecutive information abnormality from the same smart meter and raises the reliability of smart meter’s communication, having a significantly meaning to guarantee the normal function of users’ power consumption system

Exponential Input-to-State Stability for Hybrid Dynamical Networks via Impulsive Interconnection

This paper investigates the problem of exponential input-to-state stability (e-ISS) for hybrid dynamical networks (HDN) via impulsive interconnection. New concepts of input-to-state exponent property (IS-E) and augmented time are proposed for dynamical systems and hybrid systems respectively. By using IS-E estimations of nodes in HDN and methods such as multiple Lyapunov functions and hybrid time, two types of e-ISS criteria for continuous-time/discrete-time HDN are established respectively. The requirements on ISS property of every subsystem and small-gain condition for interconnection in interconnected systems or networks in the literature is relaxed. The obtained e-ISS results are extended to the case of delayed impulsive interconnection. One representative example is given to illustrate the theoretical results

Picture-specific cohort score normalization for face pair matching

Face pair matching is the task of deciding whether or not two face images belong to the same person. This has been a very active and challenging topic recently due to the presence of various sources of variation in facial images, especially under unconstrained environment. We investigate cohort normalization that has been widely used in biomet-ric verification as means to improve the robustness of face recognition under challenging environments to the face pair matching problem. Specifically, given a pair of images and an additional fixed cohort set (identities of cohort samples never appear in the test stage), two picture-specific cohort score lists are computed and the correspondent score profiles of which are modeled by polynomial regression. The extracted regression coefficients are subsequently classified using a classifier. We advance the state-of-the-art in cohort normalization by providing a better understanding of the cohort behavior. In particular, we found that the choice of the cohort set had little impact on the generalization performance. Furthermore, the larger the size of the cohort set, the more stable the system performance becomes. Experiments performed on the Labeled Faces in the Wild (LFW) benchmark show that our system achieves performance that is comparable to state-of-the-art methods