Method for secure electronic voting system: face recognition based approach

In this paper, we present an extensive evaluation of face recognition and verification approaches performed by the European COST Action MULTI-modal Imaging of FOREnsic SciEnce Evidence (MULTI-FORESEE). The aim of the study is to evaluate various face recognition and verification methods, ranging from methods based on facial landmarks to state-of-the-art off-the-shelf pre-trained Convolutional Neural Networks (CNN), as well as CNN models directly trained for the task at hand. To fulfill this objective, we carefully designed and implemented a realistic data acquisition process, that corresponds to a typical face verification setup, and collected a challenging dataset to evaluate the real world performance of the aforementioned methods. Apart from verifying the effectiveness of deep learning approaches in a specific scenario, several important limitations are identified and discussed through the paper, providing valuable insight for future research directions in the field.

Advancements in the field of biometrics have led to the development of face identification and verification systems. The problem of face verification faces many challenges; however, the challenge of progressing age for verification of human faces remains to be the most challenging of all. The work in the field of face verification across age progression is limited and the field requires more attention and research. This work proposes a technique for face verification which can handle large age gaps. The proposed technique extracts Center Symmetric Local Binary Patterns (CSLBP) to represent the features of the face image. CSLBP has been widely used as a texture descriptor in face recognition. The performance of CSLBP features when combined with weighted K-Nearest Neighbor classifier is best when compared with Support Vector Machine and Tree based classifiers. This work studies the performance of the proposed method on increasing age gaps. Usually, the performance of any face verification system deteriorates or stagnates as the age gap increases; however, the performance of the proposed method improves as the age gap increases. The experiments are performed on MORPH database.

Face recognition methods, which usually represent face images using holistic or local facial features, rely heavily on alignment. Their performances also suffer a severe degradation under variations in expressions or poses, especially when there is one gallery per subject only. With the easy access to high-resolution (HR) face images nowadays, some HR face databases have recently been developed. However, few studies have tackled the use of HR information for face recognition or verification. In this paper, we propose a pose-invariant face-verification method, which is robust to alignment errors, using the HR information based on pore-scale facial features. A new keypoint descriptor, namely, pore-Principal Component Analysis (PCA)-Scale Invariant Feature Transform (PPCASIFT)-adapted from PCA-SIFT-is devised for the extraction of a compact set of distinctive pore-scale facial features. Having matched the pore-scale features of two-face regions, an effective robust-fitting scheme is proposed for the face-verification task. Experiments show that, with one frontal-view gallery only per subject, our proposed method outperforms a number of standard verification methods, and can achieve excellent accuracy even the faces are under large variations in expression and pose.

In this paper, we study the face verification task across age by constructing a simple but powerful representation of the face which uses Local Binary Pattern (LBP) histograms. The spatial information is incorporated by constructing a hierarchical representation of the face image and computing the LBP histogram at each level. A set of most discriminative LBP features of the face are extracted using the AdaBoost learning algorithm. A strong classifier is built using a set of weak classifiers extracted and is used for classification purposes. Several experiments on the FGnet and the MORPH database were performed and the results indicate a significant improvement in the performance when compared with other discriminative approaches. Performance improvement is achieved with smaller age gaps between image pairs and it stabilizes as the age gap increases. Also, the facial hair, glasses, etc. provide discriminative cues to the system in face verification.

For the past years, studies on face recognition were focused on the effects and influence of illumination, pose, and angle to the performance, which, in fairness, has been proven by different research works as the dominant factor of adding complexity to face recognition across age progression observed through photographs, specifically headshots (Ling, Soatto, Ramanathan, & Jacobs, 2007; Ramanathan & Chellappa, 2006; Ramanathan & Chellappa, 2005). However, studies on face verification and recognition with time lapses between pairs of photographs and the interaction on the sexual orientation of the identifier was somehow defocused on the factor of effects of age progression or the time interval between pairs of facial images on face recognition. Thus, the researchers aimed to resolve the gap on the prevailing literatures on face recognition by finding the effects of age interval of the stimuli and sexual orientation of the participants on face recognition using Filipino college students as sample. Results showed that the greater the age gaps between the pair of images, the lesser the chance of identifying it. In addition, it is also not important to consider whether identifier is male or female in terms of recognizing picture because based from the result of the experiment, sexual orientation does not have an effect on the performance of the participants in face recognition. The study was guided by the theory of Biederman (1987) and Recognition By Components (RBC) to analyze the findings.

This paper proposes a novel high-order local pattern descriptor, local derivative pattern (LDP), for face recognition. LDP is a general framework to encode directional pattern features based on local derivative variations. The n(th)-order LDP is proposed to encode the (n-1)(th) -order local derivative direction variations, which can capture more detailed information than the first-order local pattern used in local binary pattern (LBP). Different from LBP encoding the relationship between the central point and its neighbors, the LDP templates extract high-order local information by encoding various distinctive spatial relationships contained in a given local region. Both gray-level images and Gabor feature images are used to evaluate the comparative performances of LDP and LBP. Extensive experimental results on FERET, CAS-PEAL, CMU-PIE, Extended Yale B, and FRGC databases show that the high-order LDP consistently performs much better than LBP for both face identification and face verification under various conditions.

Secure Learning using Face Recognition has gained significant attention due to its potential solution in curbing and elimination of electronic examination impersonation through enrollment, authentication and authorization. Despite the advancements, existing system often focuses on individual entities, void of models and special dataset to enhance security measure in comprehensively curbing electronic examination impersonation. This study aims to address this gap by performing a model using face recognition: Labeled Face in the Wild (LFW), Custom Dataset, algorithms using TensorFlowJs and Face Application Programming Interface (API) model. The research evaluates this model on a unified facial recognition dataset, assessing their performance based on face verification and recognition. The LFW dataset a popular benchmark dataset for face verification and recognition focuses on detecting and alignment of captured images, where TensorFlowJs extract face embedding, while the Face API verifies captured image by loading the Custom Dataset (Enrolled images) comparing with that of the recent before successful authentication. The LFW-TensorFlowJs combines these approaches to leverage both Secure and accurate information. Results shows that integrating LFW, TensorFlowJs and Face API model outperforms the standalone LFW.

In this paper, we present an effective facial image verification based automated attendance management system (FIV-AMS). The proposed system can be divided into three main components: face detection, image pre-processing, and face recognition. The core step of our attendance management system is the Gist feature extraction based face recognition, which can achieve three functions. Experimental results demonstrate the validity and feasibility of the proposed system by using the statical model and the dynamic model.

Great progress in face recognition technology has been made recently. Since the first face recognition vendor test (FRVT)Phillips et al. (2007) in 1993, face recognition performance has been improved by two orders of magnitude in thirteen years. Notably, in the FRVT 2006 it is the first time that algorithms are capable of human performance levels, and at false acceptance rates in the range of 0.05, machines can outperform humans Phillips et al. (2007). The advances are promising for face verification applications where a typical one-to-one match is performed. It is still a grand challenge to power large-scale face image retrieval. Large-scale face image retrieval is the enabling technology behind the next generation search engines (search beyondwords), bywhich web users can do social searchwith personal photos. High performance face identification algorithms are needed to support large scale face image retrieval. Compared with face verification, face identification is believed N times harder than face verification due to its nature of 1:N problems. The number of individuals N in the database has a great impact on both the effectiveness and efficiency of face identification algorithms. With the state of the art face identification algorithms, the identification rate is only around 70% (rank = 1) for the FERET database, a gallery of ten thousands individuals. When to serve for large-scale face image retrieval applications, the identification rate will further decrease as the gallery size increase (fortunately not linearly but logarithmically).The computing complexity of face identification is linearly related to the number of individuals N. For largescale face image retrieval the efficiency of face identification is a key issue. In this paper we focus on the efficiency aspects of face identification. Technically, it is very challenging to find a person from a very large or extremely large database which might hold face images of millions or hundred millions people. A highly efficient image retrieval technology is needed. Indexing technology based on tree structures has been widely used in commercial search engines. These structures are quite efficient for small dimensions (of the order of 1-10). However, as the data dimensionality increases, the query performance of these structures degrades rapidly. For instance, White and Jain report that as the dimensionality increases from 5 to 10, the performance of a nearest-neighbor query 2

Deep learning in face recognition is widely explored in recent times due to its ability to produce state-of-the-art results and availability of large public datasets. While recent deep learning approaches involving margin loss based image representations produce 99% accuracy across benchmarks, none of these studies focus explicitly on occluded face verification. Further, in real world scenarios, there is a need for efficient methods that cater to the cases of occlusion of faces with hats, scarves, goggle or sometimes exaggerated facial expression. Moreover, with face verification gathering traction in mainstream real-time embedded applications of surveillance, the proposed approaches need to be highly accurate. In this paper, we revisit the same through a large-scale study involving multiple synthetically created goggle-occluded face datasets using multiple state-of-the-art face representations. Through this study, we identify that occlusion in faces results in non-isotropic face representations in feature space which results in a drop in performance. Therefore, we propose an approach to enhance existing face representations by learning reference conditioned Low-Rank projections (RCLP), which can create isotropic representations thereby improving face recognition. We benchmark the developed approach over synthetically goggled versions of LFW, CFP-FP, ATT, FEI, Georgia Tech and Essex University face databases with representations from ResNet-ArcFace, VGGFace, MobilefaceNet-ArcFace LightCNN resulting in a total of 100 + experiments where we achieve improvements in the accuracy-rate across all with a maximum of 4% on FEI dataset. Finally, to validate the approach in a realistic scenario, we additionally present results over our internal face verification dataset of 1k images and confirm that the proposed approach only shows positive results without degrading existing baseline performance.

Visible face recognition systems, being vulnerable to illumination, expression, and pose, can not achieve robust performance in unconstrained situations. Meanwhile, near infrared face images, being light- independent, can avoid or limit the drawbacks of face recognition in visible light, but its main challenges are low resolution and signal noise ratio (SNR). Therefore, near infrared and visible fusion face recognition has become an important direction in the field of unconstrained face recognition research. In this paper, a novel fusion algorithm in non-subsampled contourlet transform (NSCT) domain is proposed for Infrared and visible face fusion recognition. Firstly, NSCT is used respectively to process the infrared and visible face images, which exploits the image information at multiple scales, orientations, and frequency bands. Then, to exploit the effective discriminant feature and balance the power of high-low frequency band of NSCT coefficients, the local Gabor binary pattern (LGBP) and Local Binary Pattern (LBP) are applied respectively in different frequency parts to obtain the robust representation of infrared and visible face images. Finally, the score-level fusion is used to fuse the all the features for final classification. The visible and near infrared face recognition is tested on HITSZ Lab2 visible and near infrared face database. Experiments results show that the proposed method extracts the complementary features of near-infrared and visible-light images and improves the robustness of unconstrained face recognition.

Biometric systems enable people to distinguish between physical and behavioral characteristics. Face recognition systems, a type of biometric systems, use peoples’ facial features to recognize them. The aim of this study is to perform face recognition and verification system that can run on mobile devices. The developed application is based on comparing the faces in two photographs. The user uploads two photos to the system, the system identifies the faces in these photos and performs authentication between the two faces. As a result, the system gives the output that the two faces in the photo belong to the same or different persons. It provides a security measure thanks to the face identification and verification feature included in this application. This application can be integrated into various applications and used in systems such as user login.

This paper presents a novel face recognition method by using the new image representations. While the commonly used gray-scale image is derived from the linear combination of R, G, and B color component images, the new image representations are derived from the Principal Component Analysis (PCA) tranform upon the hybrid configurations of different color component images. Compared to the correlated color space RGB, the correlations in other configurations of color components (such as RCrQ, YIQ, YCbQ, and so on) are reduced and hence the diversities among their misclassification outputs are enhanced. The new image representations, which inherit advantages from all the individual color components, are thus more invariable than the gray-scale image to the image variations for the face recognition task. Furthermore, we propose to encode the facial information from the new image representations by using an effective Local Binary Pattern (LBP) feature extraction method, which extracts and fuses the multi-resolution LBP features. Finally, the resulting LBP features undergo the Fisher discriminant analysis for face recognition. The most challenging Face Recognition Grand Challenge (FRGC) version 2 Experiment 4 shows the proposed method, which achieves the face verification rate of 83.41% at the false accept rate of 0.1%, performs better than some recent face recognition methods.

This paper presents an automatic video based face verification and recognition system by Support Vector Machines (SVMs). Faces as training samples are automatically extracted from input video sequences in real-time by LUT-based Adaboost and are normalized both in geometry and in gray level distribution after facial landmark localization via Simple Direct Appearance Model (SDAM). Two different strategies for multi-class face verification and recognition problems with SVMs, one-vs-all and one-vs-another, are discussed and compared in details. Experiment results over 100 clients are reported to demonstrate the effectiveness of SVM on video sequences.

A comparative study on local binary pattern (LBP) based face recognition: LBP histogram versus LBP image