Cosine Similarity Metric Learning for Face Verification

The initial phase in an automatic facial analysis is face detection, which has been concentrated in the course of recent decades. It is very challenging to recognize a face from an image due to illumination, variations in pose, occlusion, scaling and facial expression. In our work, we propose a methodology to detect a face with arbitrary pose variations. First, a Normalized Pixel Difference (NPD) feature is calculated from the face image. Second, the optimal subset of NPD features and their combinations are refined via Deep Quadratic Tree (DQT). In order to learn the NPD feature based deep quadratic trees, Discrete Adaboost classifier is adopted. The experimental results on Labeled Faces in the Wild (LFW) face dataset demonstrate that our framework performs better than the previous methods in detecting multi-view faces.

Face identification aims at putting a label on an unknown face with respect to some training set. Unconstrained face identification is a challenging problem because of the possible variations in face pose, illumination, occlusion, and facial expression. This paper presents an unconstrained face identification method based on face frontalization and learning-based data representation. Firstly, the frontal views of unconstrained face images are automatically generated by using a single, unchanged 3D face model. Then, we crop the face relevant regions of the frontal views to segment faces from the backgrounds. At last, to enhance the discriminative capability of the coding vectors, a support vector-guided dictionary learning (SVGDL) model is applied to adaptively assign different weights to different pairs of coding vectors. The performance of the proposed method FSVGDL (frontalization-based support vector guided dictionary learning) is evaluated on the Labeled Faces in the wild (LFW) database. After decision fusion, the identification accuracy yields 97.17% when using 7 images per individual for training and 3 images per individual for testing with 158 classes in total.

Fine-grained face verification: FGLFW database, baselines, and human-DCMN partnership

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.

This research features a new method for automatic face recognition robust to variations in lighting, facial expression and eyewear. The new algorithm named SKKUfaces (Sungkyunkwan University faces) employs PCA (Principal Component Analysis) and FLD (Fisher's Linear Discriminant) in series similarly to Fisherfaces. The fundamental difference is that SKKUfaces effectively eliminates, in the reduced PCA subspace, portions of the subspace that are responsible for variations in lighting and facial expression and then applies FLD to the resulting subspace. This results in superb discriminating power for pattern classification and excellent recognition accuracy. We also propose an efficient method to compute the between-class scatter and within-class scatter matrices for the FLD analysis. We have evaluated the performance of SKKUfaces using YALE and SKKU facial databases. Experimental results show that the SKKUface method is computationally efficient and achieves much better recognition accuracy than the Fisherface method [1] especially for facial images with variations in lighting and eyewear.

Facial recognition has become an important technology in the domain of biometric authentication, thereby providing greater protection to access control and identity verification applications. This work presents a facial verification system that is new and based on a Siamese neural network. Its operation is based on having deep convolutional architectures that extract strong feature embeddings for facial images. The proposed system employs the Labeled Faces in the Wild (LFW) dataset for training negative samples and was further enhanced using custom-captured positive and anchor images taken from the webcam with extensive data augmentation to mimic real-life variations in illumination, pose, and occlusion. The architecture of the network consists of an embedding layer to extract 4096-dimensional feature vectors, an L1 distance layer that measures the similarity between pairs of images, and a classification layer to predict the verification outcomes using a binary cross-entropy loss function optimized through the Adam algorithm. Experimental results indicate high precision and recall, showing near-unity performance on benchmark tests and real-time verification scenarios. Those results further highlight the capability of Siamese networks for one-shot learning in biometric applications, minimizing their dependence on large labeled datasets. Future work will focus on the scalability to larger and more heterogeneous datasets and integration with multimodal biometric systems. Generally, this research presents a promising framework for secure, efficient, and robust facial verification for real-world applications.

This work presents a method to automatically locate facial feature points under large variations in pose, illumination and facial expressions. First we propose a method to calculate probabilistic-like output for each pixel of image. This probabilistic-like output describes the possibility of the pixel to be the center of specified object. A Gaussian mixture model is used to approximate the distribution of probabilistic-like output. The centers of these Gaussians are assigned with a probabilistic-like measure and they are considered as candidate feature points. There might be one or more candidate feature points in each facial region. A 3D model of facial feature points is built to enforce constraints on the localization results of feature points. Compared with active shape model (ASM) and its variant methods, our method could accommodate larger variations in pose, lighting and face expressions. Moreover, it is less sensitive to initialization errors, accurate, and fast. It takes a computer with P4 CPU about 10 ms to locate the five feature points (two eye centers, two mouth corners and nose tip). The feature localization accuracy is comparable with the accuracy of manually labeled features and it is robust to noise (glasses, beards). Experiments on FERET gallery and PIE are reported in this paper as well.

Face recognition remains a crucial issue in computer vision with various applications. This paper introduces an adapted method to tackle the challenges posed by mask-wearing during the COVID-19 pandemic. We propose modifications to the bag of convolutional neural networks (BoCNN) framework, which combines CNNs and the bag of words (BoW) approach. Our main contribution is customizing the BoCNN algorithm to identify faces with masks by focusing on the visible facial regions, particularly the eyes and eyebrows. Facial landmarks are detected, and the region of interest (ROI) is extracted using techniques such as Media Pipe. A pre-trained CNN is then applied to sections within the ROI, enabling robust feature extraction that captures intricate details such as lighting variations and facial expressions while reducing the impact of mask occlusions. The extracted features are pooled to create a comprehensive representation for recognition. Extensive experiments on the labeled faces in the wild (LFW) dataset, including masked face images, demonstrate the superiority of our adapted BoCNN approach over traditional BoW and deep feature extraction methods, especially accurately recognizing masked faces. Additionally, we assess the generalizability of our method across multiple datasets and discuss potential limitations and future research directions. The proposed BoCNN-based solution proves effective in recognizing masked faces, making it highly relevant for applications in security, human-computer interaction, and various other domains affected by the COVID-19 pandemic.

In this paper performance of elastic bunch graph matching (EBGM) for face recognition under variation in facial expression, variation in lighting condition and variation in poses are given. In this approach faces are represented by labelled graphs. Experimental results of EBGM on ORL, Yale B and FERET datasets are provided. Strong and weak features of EBGM algorithm are discussed.

This research aims to develop artificial intelligence (AI) algorithms in the context of facial recognition with a focus on increasing accuracy in difficult environmental conditions. Although facial recognition technology has made great progress, challenges such as poor lighting, variations in facial expressions, and head rotation are still problems that must be overcome. The research methodology involved collecting a wide dataset covering a wide variety of faces under various environmental conditions. This data is then processed and its features are extracted using computer image processing techniques. Furthermore, several deep neural network architectures, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), were developed, trained, and evaluated for face recognition tasks. The expected result is the development of an AI algorithm that is able to overcome challenges in facial recognition with higher accuracy than existing methods. In particular, significant improvements in facial recognition accuracy are expected especially under low lighting conditions and variations in facial expressions. This research has a major impact in a variety of security applications, such as border surveillance, building access control, and corporate security. With higher facial recognition accuracy, security risks can be significantly reduced, resulting in safer and more efficient security solutions. In conclusion, this research aims to bring innovation in facial recognition technology through advanced AI approaches, with the potential to improve security in various contexts.

Face verification is the task of determining by analyzing face images, whether a person is who he/she claims to be. It is a very challenge problem, due to large variations in lighting, background, expression, hairstyle and occlusion. The crucial problem is to compute the similarity of two face vectors. Metric learning has provides a viable solution to this problem. Until now, many metric learning algorithms have been proposed, but they are usually limited to learning a linear transformation (i.e. finding a global Mahalanobis metric). In this brief, we propose a nonlinear metric learning method, which learns an explicit mapping from the original space to an optimal subspace, using deep Independent Subspace Analysis network. Compared to kernel methods, which can also learn nonlinear transformations, our method is a deep and local learning architecture, and therefore exhibits more powerful ability to learn the nature of highly variable dataset. We evaluate our method on the LFW benchmark, and results show very comparable performance to the state-of-art methods (achieving 92.28% accuracy), while maintaining simplicity and good generalization ability.

Face representation and matching are two essential issues in face verification task. Various approaches have been proposed focusing on these two issues. However, few of them addressed the joint optimal solutions of these two issues in a unified framework. In this paper, we present a second‐order face representation method for face pair and a unified face verification framework, in which the feature extractors and the subsequent binary classification model design can be selected flexibly. Our contributions can be summarized in the following aspects. First, a novel face‐pair representation method that employs the second‐order statistical property of the face pairs is proposed, which retains more information compared to the existing methods. Second, a flexible binary classification model, which differs from the conventionally used metric learning, is constructed based on the new face‐pair representation. Finally, we verify that our proposed face‐pair representation can benefit from large training datasets. All the experiments are carried out on Labeled Face in the Wild (LFW) to verify the algorithm’s effectiveness against challenging uncontrolled conditions.

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.

A lot of higher-order computer vision applications, like face recognition and facial expression analysis, depend on the precise detection of landmarks inside facial pictures. Although landmark localization is a straightforward and intuitive operation for humans, for it to be replicated in machines, it has taken multiple years of research, giving rise to large number of high-quality image data sets, and an abrupt increase in the processing capacity of computers to attain near-human proficiency. While recognizing physical characteristics on a face, such as types of noses, is an intuitive and ordinary task for human vision, it has proven to be quite tough for computer vision, as it has not profited from millennia of evolution. Although facial images generally have similar content, common variations in pose, lighting, facial expression, and facial features can give rise to a multitude of problems for many computer vision systems, leading to substantial errors and mistakes in the task of computer vision face landmarking accuracy. In this study, we examine the two facial landmark identification techniques already in use-the Mediapipe Face Landmarker and Dlib's 68-point face landmark detection algorithm-to determine the conditions in which each method performs best.

We develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take advantage of the observation that the images of a particular face, under varying illumination but fixed pose, lie in a 3D linear subspace of the high dimensional image space-if the face is a Lambertian surface without shadowing. However, since faces are not truly Lambertian surfaces and do indeed produce self-shadowing, images will deviate from this linear subspace. Rather than explicitly modeling this deviation, we linearly project the image into a subspace in a manner which discounts those regions of the face with large deviation. Our projection method is based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variation in lighting and facial expressions. The eigenface technique, another method based on linearly projecting the image space to a low dimensional subspace, has similar computational requirements. Yet, extensive experimental results demonstrate that the proposed "Fisherface" method has error rates that are lower than those of the eigenface technique for tests on the Harvard and Yale face databases.