Data and Algorithms for End-to-End Thermal Spectrum Face Verification

Although face verification systems have proven to be reliable in ideal environments, they can be very sensitive to real environmental conditions. The system robustness can be increased by the fusion of different face verification algorithms. To the best of our knowledge, no face verification system tried exploiting the fusion of LDA and PCA. In our opinion, the apparent strong correlation of LDA and PCA, especially when frontal views are used and PCA is applied before LDA, discouraged the fusion of such algorithms. In this paper, we show that PCA and LDA can be fused with some simple strategies, and such fusion allows outperforming the best individual verification algorithm based on PCA or LDA.

Recent year, the method based on face verification has been one of the most vibrant study aspect in face verification field. The problem of face verification can be defined: imputing the picture or video, identifying or validating by using face database. video-video face verification means inputting video setting, verification by using face video data。It has most useful information, including: a lots of pictures of one person, continuity of face in time and space in video, three dimension face model etc. 1.PCA face verification Recent year, the method based on face verification has been one of the most vibrant study aspect in face verification field. The problem of face verification can be defined: imputing the picture or video, identifying or validating by using face database. video-video face verification means inputing video setting, verification by using face video data。It has most useful information, including: a lots of pictures of one person, continuity of face in time and space in video, three dimension face model etc. The way that face information be described in video in present literature can be summarized as follows: vector、matrix probability、 dynamic model 、fashion. daopting modes of probability and fashion need mass swatch which reflect distribution of face, can accurately describe face distribution. Dynamic model can better use time and space information , but the method is compicated, calculation is great. The describe way of imputing vector has a big defect, the randomicity of choose swatch.the way of matrix is comparatively simple, can apply the informatin of discontinuous pictures in time,but how to depict relationship between matrix is a worthful contect. Face character based on PCA (Principal Component Analysis, PCA)is the classic arithmetic in face verification, it is a face face veification and describe technology educe from bases analysis. Face character is a simple 、fast、 useful、 arithmetic based on transform coefficient character. Face verification: compare the given face portrait with the picture stored in computer, determine the face is whether the appointed guy, it is a one to one matching process, usually one person stored more face portrait in different angle in computer. A face verification system based on video including face detect model face portrait pretreatment model face character distill model and face verification model. 1.1PCA face verification Face verification: compare the given face portrait with the picture stored in computer, determine the face is whether the appointed guy, it is a one to one matching process, usually one person stored more face portrait in different angle in computer. A face verification system (3) based on video including face detect model face portrait pretreatment model face character distill model and face verification model,as picture 1.

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.

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.

Face verification is a very challenge problem, due to large variations in expression, background, pose, and occlusion. It involves two crucial problems, one is face representation and the other is the similarity computation of face vectors. Addressing the two problem, this paper proposes a method for simultaneously learning features and a corresponding similarity metric for a real world face verification, which apply novel regularization to learn a nonlinear metric learning with deep convolution neural network. Experimental results on the widely used LFW dataset are presented to show the effectiveness of the proposed method.

The face verification systems gained significant attention in the last few years due to the increased security concern in public and private places. Face detection is the most important and initial stage in the automatic face verification system. It helps to determine the existence of faces in an image and return the position and location of the face. The face verification system's accuracy depends on face detection. The human faces are not always frontal and have many variations, therefore, face detection is challenging in unconstrained scenarios. One main challenge of face detection is occlusion. The proposed work is an attempt to illustrate the cognitive informatics approach using machine learning and present an occluded face detection method. The proposed method uses Adaboost[1] machine learning approach. The Viola-Jones[2] algorithm along with free rectangular features[3] has been adopted in the proposed approach in order to detect faces. the machine learning methods require two operation namely training and testing. Two cascade classifiers are used in which one is trained on holistic faces and the second is trained on half occluded faces; both of the classifiers are used in parallel to work in unconfined scene. Additionally, for improvement the correctness and adeptness of the system, the skin color models are applied which are used for removing of the false positive detection. The experiment has been performed on FDDB[4] dataset. The results shows that the proposed method achieve desirable results in the detection of half occluded faces.

Face verification for on line application is a difficult problem and many researchers have tried to solve it by convolutional neural network. Among of them, most works used the last-hidden layer as the feature of face, and abandoned the features in the lower layers which indicate local information. To remedy this, we extract features of all layers in the convolutional neural network, and fuse these features together after dimensionality reduction with PCA. Then these features are utilized for face verification with neural network classifier. Experiment results show that complete features can improve the verification rate effectively than using the last-hidden layer only.

Many developments have taken place in the field of face-recognition and liveness analysis to improvise various device securities and attendance verification systems. Many approaches have incorporated 3D analysis of the face to predict the liveness of the person in front of it. Our method tries to account for this problem without using advanced 3D imaging techniques or hardware. This results in a solution that is both, more economical and also much easier to deploy. It consists of two parts; the former helps in face verification and the latter to check the liveness of the face. In the first part, we have used a model based on Google’s FaceNet Model which learns a mapping from face images to compact Euclidean space distances, which directly correspond to the measure of similarity of the images. Once the space has been produced, face verification can be easily implemented using standard techniques with embeddings as feature vectors. For the second part, we have employed a cascaded multi-task framework that extracts certain features from the facial image which are then used to check for liveness by tracking their relative displacements. These extracted features were used to check the liveness of the person’s face by asking them to perform some tasks in a random order like head and facial movements etc.

In this paper face verification techniques have been performed over 3D data acquired by a Laser Scanner. Advantages of 3D face models have been used to perform a normalization task over each face. First, an original method to detect local features points, based on Spin Images, has been developed. Once local features, as the nose tip or eyes corners, have been detected, a normalization process is carried out. After face normalization, different depth maps are calculated using several transform functions to equalize the images. The adequacy of each equalization to face verification has been measured to determine which one emphasizes most the feature discrimination. Face verification has been performed through a Principal Component Analysis and a Support Vector Machine. Final results show the importance of a careful 3D normalization and an optimal election of the depth map towards a improvement in the verification method.

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.

Most existing state-of-the-art deep learning algorithms discover sophisticated representations in huge datasets using convolutional neural networks (CNNs) that mainly adopt backpropagation (BP) algorithm as the backbone for training the face recognition problems. However, since decades ago, BP has been debated for causing trivial issues such as iterative gradient-descent operation, slow convergence rate, local minima, intensive human intervention, exhaustive computation, time-consuming, and so on. On the other hand, a competitive machine learning algorithm called extreme learning machine (ELM) emerged with extreme fast implementation and simple in theory has overcome the challenges faced by BP. The ELM advocates the convergence of machine learning and biological learning for pervasive learning and intelligence and has been extensively researched in widespread applications. Nonetheless, till date, none of the work of ELM has proved its competency in tackling face verification problem. Hence, in this paper, we are going to probe for the first time the feasibility of ELM-based network in handling the face verification task. We devise and propose a novel and distinguished hybrid local receptive field-based extreme learning machine with DeepID (hereinafter denoted as H-ELM-LRF-DeepID), to discriminate face pairs. The experimental results on the YouTube face database, labeled faces in the wild (LFW), and CelebFaces datasets have shed light upon the feasibility and usefulness of the H-ELM-LRF-DeepID in the face verification task.

In this work, we present a bimodal biometric system using speech and face features and tested its performance under degraded condition. Speaker verification (SV) system is built using Mel-Frequency Cepstral Coefficients (MFCC) followed by delta and delta-delta for feature extraction and Gaussian Mixture Model (GMM) for modeling. A face verification (FV) system is built using the combination of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Sum rule is used for the fusion of the biometric scores. The performance of SV system under degraded condition is also checked. All the experimental results are shown upon a subset of IITG-DIT M4 multi-biometric database. The complementary information derived from the speech biometric at training stage is used to further decrease the FV error rate, which is termed as Cohort fed FV system. Finally we propose an improved bimodal person authentication system using SV and Cohort fed FV biometric systems.

We present a method to combine the Fisher vector representation and the Deep Convolutional Neural Network (DCNN) features to generate a rerpesentation, called the Fisher vector encoded DCNN (FV-DCNN) features, for unconstrained face verification. One of the key features of our method is that spatial and appearance information are simultaneously processed when learning the Gaussian mixture model to encode the DCNN features. Evaluations on two challenging verification datasets show that the proposed FV-DCNN method is able to capture the salient local features and also performs well when compared to many state-of-the-art face verification methods.

Clothescounter: A framework for star-oriented clothes mining from videos

Deep convolutional neural networks (DCNNs) are currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning. The success of DCNNs can be attributed to the careful selection of their building blocks (e.g., residual blocks, rectifiers, sophisticated normalization schemes, to mention but a few). In this paper, we propose Π-Nets, a new class of function approximators based on polynomial expansions. Π-Nets are polynomial neural networks, i.e., the output is a high-order polynomial of the input. The unknown parameters, which are naturally represented by high-order tensors, are estimated through a collective tensor factorization with factors sharing. We introduce three tensor decompositions that significantly reduce the number of parameters and show how they can be efficiently implemented by hierarchical neural networks. We empirically demonstrate that Π-Nets are very expressive and they even produce good results without the use of non-linear activation functions in a large battery of tasks and signals, i.e., images, graphs, and audio. When used in conjunction with activation functions, Π-Nets produce state-of-the-art results in three challenging tasks, i.e., image generation, face verification and 3D mesh representation learning. The source code is available at https://github.com/grigorisg9gr/polynomial_nets.