Challenges In Face Recognition Research Articles

Enhancing Face Recognition Dataset using Generative Artificial Intelligence: Review Study

Face recognition is a fast growing and crucial area of study due to its applicability in different life applications. Face recognition systems often face significant challenges due to poor-quality datasets, which can result from factors such as low illumination, low resolution, facial distortions, and varying environmental conditions. These limitations hinder the performance and reliability of face recognition technologies. Generative Adversarial Networks (GANs) have emerged as a powerful tool to address these issues by enhancing image quality and generating realistic facial representations under challenging conditions. This review paper explores various GAN-based models each designed to tackle specific face recognition challenges. These models demonstrate significant improvements in enhancement ratios, ranging from 75% to 97%, by leveraging advanced generative techniques to reconstruct and augment facial images. Beyond face recognition, the methodologies discussed in this research have broader implications for enhancing image datasets in other domains, such as medical imaging, surveillance, and autonomous driving, where poor data quality is a common issue. By improving dataset quality through generative AI, this research paves the way for more robust and accurate machine learning applications across diverse fields.

Improving Similar Face Recognition Using ResNet-50 and FPN with Triplet Loss

Identical twin recognition has been attracting attention as a challenge in face recognition. In this paper, the method of FPN combined with ResNet-50 and triplet loss function is used to achieve better recognition ability on similar face datasets including identical twins without affecting much the performance of ordinary face datasets. In order to illustrate the role of FPN, the author conducted a series of comparative experiments. With or without FPN as a variable, the experiment concluded that ResNet-50 combined with FPN enhanced the recognition ability of similar faces, and the author listed the reasons that may have led to this result. Subsequently, under the condition of different training parameter settings, before overfitting, the learning rate and the number of iterations were positively correlated with the performance of the model on the ordinary faces dataset and negatively correlated with that on the similar faces dataset (including identical twins). The performance of the model on the ordinary face dataset is negatively correlated with the performance on the similar face dataset. Finally, a relatively good set of parameters is decided among the three sets tested.

Face Recognition Based on Deep Learning: A Comprehensive Review

Face recognition technology has undergone transformative changes with the advent of deep learning techniques. This review paper provides a comprehensive examination of the development and current state of face recognition techniques influenced by deep learning. We begin by discussing the fundamental deep learning models that have dramatically enhanced the accuracy and efficiency of face recognition, highlighting pivotal architectures such as convolutional neural networks (CNNs) and autoencoders. Subsequent sections delve into the application of these models in various environments and challenges, such as different lighting conditions, occlusion, and facial expressions. We also address the integration of deep learning with emerging technologies such as 3D facial reconstruction and multimodal biometrics. Furthermore, the review explores the ethical, privacy, and bias concerns inherent in facial recognition systems, focusing on the need for responsible and fair practices in AI. Finally, future directions are suggested, focusing on the need for robust, adaptable, and ethical face recognition systems. This paper aims to provide an important resource for researchers and practitioners in the field of computer vision, providing insight into the technological advances and ongoing challenges in deep learning-based face recognition.

Facial mask-wearing prediction and adaptive gender classification using convolutional neural networks

The world has lived an exceptional time period caused by the Coronavirus pandemic. To limit Covid-19 propagation, governments required people to wear a facial mask outside. In facial data analysis, mask-wearing on the human face creates predominant occlusion hiding the important oral region and causing more challenges for human face recognition and categorisation. The appropriation of existing solutions by taking into consideration the masked context is indispensable for researchers. In this paper, we propose an approach for mask-wearing prediction and adaptive facial human-gender classification. The proposed approach is based on convolutional neural networks (CNNs). Both mask-wearing and gender information are crucial for various possible applications. Experimentation shows that mask-wearing is very well detectable by using CNNs and justifies its use as a prepossessing step. It also shows that retraining with masked faces is indispensable to keep up gender classification performances. In addition, experimentation proclaims that in a controlled face-pose with acceptable image quality' context, the gender attribute remains well detectable. Finally, we show empirically that the adaptive proposed approach improves global performance for gender prediction in a mixed context.

FRCSyn-onGoing: Benchmarking and comprehensive evaluation of real and synthetic data to improve face recognition systems

FRCSyn-onGoing: Benchmarking and comprehensive evaluation of real and synthetic data to improve face recognition systems

A Comprehensive Review of Face Recognition Techniques, Trends and Challenges

A Comprehensive Review of Face Recognition Techniques, Trends and Challenges

HAMFace: Hardness adaptive margin loss for face recognition with various intra-class variations

HAMFace: Hardness adaptive margin loss for face recognition with various intra-class variations

A narrow band of image dimensions is critical for face recognition

A key challenge in human and computer face recognition is to differentiate information that is diagnostic for identity from other sources of image variation. Here, we used a combined computational and behavioural approach to reveal critical image dimensions for face recognition. Behavioural data were collected using a sorting and matching task with unfamiliar faces and a recognition task with familiar faces. Principal components analysis was used to reveal the dimensions across which the shape and texture of faces in these tasks varied. We then asked which image dimensions were able to predict behavioural performance across these tasks. We found that the ability to predict behavioural responses in the unfamiliar face tasks increased when the early PCA dimensions (i.e. those accounting for most variance) of shape and texture were removed from the analysis. Image similarity also predicted the output of a computer model of face recognition, but again only when the early image dimensions were removed from the analysis. Finally, we found that recognition of familiar faces increased when the early image dimensions were removed, decreased when intermediate dimensions were removed, but then returned to baseline recognition when only later dimensions were removed. Together, these findings suggest that early image dimensions reflect ambient changes, such as changes in viewpoint or lighting, that do not contribute to face recognition. However, there is a narrow band of image dimensions for shape and texture that are critical for the recognition of identity in humans and computer models of face recognition.

A simple and effective patch-Based method for frame-level face anti-spoofing

A simple and effective patch-Based method for frame-level face anti-spoofing

A feature map aggregation network for unconstrained video face recognition

Unconstrained video face recognition is an extension of face recognition technology, and it is an indispensable part of intelligent security and criminal investigation systems. However, general face recognition technology cannot be directly applied to unconstrained video face recognition, because the video contains fewer frontal face image frames and a single image contains less face feature information. To address the above problems, this work proposes a Feature Map Aggregation Network (FMAN) to achieve unconstrained video face recognition by aggregating multiple face image frames. Specifically, an image group is used as the input of the feature extraction network to replace a single image to obtain a multi-channel feature map group. Then a quality perception module is proposed to obtain quality scores for feature maps and adaptively aggregate image features from image groups at the feature map level. Finally, extensive experiments are conducted on the challenging face recognition benchmarks YTF, IJB-A and COX to evaluate the proposed method, showing a significant increase in accuracy compared to the state-of-the-art.

Broad Learning System for Tackling Emerging Challenges in Face Recognition

Face recognition has been rapidly developed and widely used. However, there is still considerable uncertainty in the computational intelligence based on human-centric visual understanding. Emerging challenges for face recognition are resulted from information loss. This study aims to tackle these challenges with a broad learning system (BLS). We integrated two models, IR3C with BLS and IR3C with a triplet loss, to control the learning process. In our experiments, we used different strategies to generate more challenging datasets and analyzed the competitiveness, sensitivity, and practicability of the proposed two models. In the model of IR3C with BLS, the recognition rates for the four challenging strategies are all 100%. In the model of IR3C with a triplet loss, the recognition rates are 94.61%, 94.61%, 96.95%, 96.23%, respectively. The experiment results indicate that the proposed two models can achieve a good performance in tackling the considered information loss challenges from face recognition.

Face Recognition challenges due to aging: A Review

Face Recognition challenges due to aging: A Review

Face recognition challenges due to aging: a review

Face recognition challenges due to aging: a review

Disguised heterogeneous face recognition using deep neighborhood difference relational network

Disguised heterogeneous face recognition using deep neighborhood difference relational network

Dual Gaussian Modeling for Deep Face Embeddings

• framework using dual Gaussian distributions to model face embeddings with uncertainty. • Learn a sample-specific weight adaptively to integrate sub-Gaussian features into a compact representation. • Propose to minimize weighted Euclidean distance and the entropy of adaptive weight to constrain the two distributions. • Perform comprehensive experiments and analyses to illustrate the effect of our method for challenging face recognition. Most currently existing face recognition methods model face images as deterministic points in the latent space. However, it could encounter performance drop inevitably in a fully unconstrained scenario for the intrinsic noise of the images. In order to mitigate the detrimental impact of noisy data on model training, distribution estimation is introduced to face recognition, which models each face image as a Gaussian distribution and improves the robustness against noise effectively. But the uncertainty (variance) learned only relates to one attribute: the quality of the image. We propose dual Gaussian modeling (DGM) for deep face embeddings. For an input image, the network learns two Gaussian distributions simultaneously. The main Gaussian branch focuses on learning easy samples in the training dataset, while the other one mainly deals with faces with large pose. The uncertainty is not only correlated with image quality, but also with facial pose. During training, a sample-specific adaptive weight is learned to integrate the two sub-Gaussian features into a more compact discriminant embedding for classification . Besides, we introduce weighted Euclidean distance and minimize the entropy of the adaptive weight to regulate the relationship between the two distributions. Comprehensive experiments and analysis demonstrate that our method can boost the performance of face recognition under common or more unconstrained benchmarks, such as IJB-C.

Learning Representations for Face Recognition: A Review from Holistic to Deep Learning

For decades, researchers have investigated how to recognize facial images. This study reviews the development of different face recognition (FR) methods, namely, holistic learning, handcrafted local feature learning, shallow learning, and deep learning (DL). With the development of methods, the accuracy of recognizing faces in the labeled faces in the wild (LFW) database has been increased. The accuracy of holistic learning is 60%, that of handcrafted local feature learning increases to 70%, and that of shallow learning is 86%. Finally, DL achieves human-level performance (97% accuracy). This enhanced accuracy is caused by large datasets and graphics processing units (GPUs) with massively parallel processing capabilities. Furthermore, FR challenges and current research studies are discussed to understand future research directions. The results of this study show that presently the database of labeled faces in the wild has reached 99.85% accuracy.

A New Design of Occlusion Invariant Face Recognition Using Optimal Pattern Extraction and CNN with GRU-Based Architecture

Deep learning networks are considered as an important technique for face recognition and image recognition. Convolutional Neural Networks (CNN) is regarded as a problem solver in face recognition challenges. To solve the challenges of occlusion and noise in the image, more clarification is needed to acquire high accuracy. Hence, a deep learning model is developed in this paper. The proposed model covers four main steps: (a) Data acquisition, (b) Pre-processing, (c) pattern extraction, and (d) classification. The benchmark datasets with occluded faces is gathered from public source. Further, the pre-processing of the images is performed by contrast enhancement and Gabor filtering. With these pre-processed images, pattern extraction is done by the optimal local mesh ternary pattern. By inputting the pattern extracted image, a deep learning model “CNN with Gated Recurrent Unit (GRU)” network performs the recognition process. The experimental results are obtained and the proposed model gives better classification accuracy.

A New Design of Occlusion-Invariant Face Recognition Using Optimal Pattern Extraction and CNN with GRU-Based Architecture

Face detection is a computer technology being used in a variety of applications that identify human faces in digital images. In many face recognition challenges, Convolutional Neural Networks (CNNs) are regarded as a problem solver. Occlusion is determined as the most common challenge of face recognition in realistic applications. Several studies are undergoing to obtain face recognition without any challenges. However, the occurrence of noise and occlusion in the image reduces the achievement of face recognition. Hence, various researches and studies are carried out to solve the challenges involved with the occurrence of occlusion and noise in the image, and more clarification is needed to acquire high accuracy. Hence, a deep learning model is intended to be developed in this paper using the meta-heuristic approach. The proposed model covers four main steps: (a) data acquisition, (b) pre-processing, (c) pattern extraction and (d) classification. The benchmark datasets regarding the face image with occlusion are gathered from a public source. Further, the pre-processing of the images is performed by contrast enhancement and Gabor filtering. With these pre-processed images, pattern extraction is done by the optimal local mesh ternary pattern. Here, the hybrid Whale–Galactic Swarm Optimization (WGSO) algorithm is used for developing the optimal local mesh ternary pattern extraction. By inputting the pattern-extracted image, the new deep learning model namely “CNN with Gated Recurrent Unit (GRU)” network performs the recognition process to maximize the accuracy, and also it is used to enhance the face recognition model. From the results, in terms of accuracy, the proposed WGSO-[Formula: see text] model is better by 4.02%, 3.76% and 2.17% than the CNN, SVM and SRC, respectively. The experimental results are presented by performing their comparative analysis on a standard dataset, and they assure the efficiency of the proposed model. However, many challenging problems related to face recognition still exist, which offer excellent opportunities to face recognition researchers in the future.

A comprehensive survey on 3D face recognition methods

3D face recognition (3DFR) has emerged as an effective means of characterizing facial identity over the past several decades. Depending on the types of techniques used in recognition, these methods are categorized into traditional and modern. The former generally extract distinctive facial features (e.g. global, local, and hybrid features) for matching, whereas the latter rely primarily on deep learning to perform 3DFR in an end-to-end way. Many literature surveys have been carried out reviewing either traditional or modern methods alone, while only a few studies are conducted simultaneously on both of them. This survey presents a state-of-the-art for 3DFR covering both traditional and modern methods, focusing on the techniques used in face processing, feature extraction, and classification. In addition, we review some specific face recognition challenges, including pose, illumination, expression variations, self-occlusion, and spoofing attack. The commonly used 3D face datasets have been summarized as well. • Illustrate and compare 3D face recognition covering traditional and modern methods. • Focus on the techniques used in feature extraction and classification. • Review specific FR challenges, including pose, expression variations, and occlusion. • Summarize commonly used 3D face datasets.

Masked Face Recognition From Synthesis to Reality

As we have been seriously hit by the COVID-19 pandemic, wearing a facial mask is a crucial action that we can take for our protection. This paper reports a comprehensive study on the recognition of masked faces. By using facial landmarks, we synthesize the facial mask for each face in several benchmark databases with different challenging factors. The IJB-B and IJB-C databases are selected for evaluating the performance against the variation across pose, illumination and expression (PIE). The FG-Net database is selected for evaluating the performance across age. The SCface is chosen for evaluating the performance on low-resolution images. The MS-1MV2 is exploited as the base training set. We use the ResNet-100 as the feature embedding network connected to state-of-the-art loss functions designed for tackling face recognition. The loss functions considered include the Center Loss, the Marginal Loss, the Angular Softmax Loss, the Large Margin Cosine Loss and the Additive Angular Margin Loss. Both verification and identification are conducted in our evaluation. The performances for recognizing faces with and without the synthetic masks are all evaluated for a complete comparison. The network with the best loss function for recognizing synthetic masked faces is then assessed on a real masked face database, the cleaned RMFRD (c-RMFRD) dataset. Compared with a human user test on the c-RMFRD, the network trained on the synthetic masked faces outperforms human vision for a large gap. Our contributions are fourfold. The first is a comprehensive study for tackling masked face recognition by using state-of-the-art loss functions against various compounding factors. For comparison purpose, the second is another comprehensive study on the recognition of faces without masks by using the same loss functions against the same challenging factors. The third is the verification of the network trained on synthetic masked faces for tackling the real masked face recognition with performance better than human inspectors. The fourth is the highlight on the challenges of masked face recognition and the directions for future research. Our code, trained models and dataset are available via the project GitHub site.