face recognition research papers 2022

3D face recognition: A comprehensive survey in 2022

• Review Article
• Open access
• Published: 05 August 2023
• Volume 9 , pages 657–685, ( 2023 )

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• Yaping Jing 1 ,
• Xuequan Lu 1 &
• Shang Gao 1  

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In the past ten years, research on face recognition has shifted to using 3D facial surfaces, as 3D geometric information provides more discriminative features. This comprehensive survey reviews 3D face recognition techniques developed in the past decade, both conventional methods and deep learning methods. These methods are evaluated with detailed descriptions of selected representative works. Their advantages and disadvantages are summarized in terms of accuracy, complexity, and robustness to facial variations (expression, pose, occlusion, etc.). A review of 3D face databases is also provided, and a discussion of future research challenges and directions of the topic.

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Yaping Jing, Xuequan Lu & Shang Gao

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Yaping Jing received her bachelor degree (Hons) in information technology from Deakin University, Australia in 2016. She is currently a Ph.D. candidate in the School of Information Technology at Deakin University. Her research interests include 3D face recognition, 3D data processing, and machine learning.

Xuequan Lu is a lecturer (assistant professor) in Deakin University, Australia. He spent more than two years working research fellow in Singapore. Prior to that, he received his Ph.D. degree from Zhejiang University, China in 2016. His research interests mainly fall into the category of visual data computing, for example, geometry modeling, processing and analysis, animation, simulation, 2D data processing and analysis.

Shang Gao received her Ph.D. degree in computer science from Northeastern University, China in 2000. She is currently a senior lecturer in the School of Information Technology, Deakin University. Her current research interests include cybersecurity, cloud computing, and machine learning.

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Jing, Y., Lu, X. & Gao, S. 3D face recognition: A comprehensive survey in 2022. Comp. Visual Media 9 , 657–685 (2023). https://doi.org/10.1007/s41095-022-0317-1

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Title: eight years of face recognition research: reproducibility, achievements and open issues.

Abstract: Automatic face recognition is a research area with high popularity. Many different face recognition algorithms have been proposed in the last thirty years of intensive research in the field. With the popularity of deep learning and its capability to solve a huge variety of different problems, face recognition researchers have concentrated effort on creating better models under this paradigm. From the year 2015, state-of-the-art face recognition has been rooted in deep learning models. Despite the availability of large-scale and diverse datasets for evaluating the performance of face recognition algorithms, many of the modern datasets just combine different factors that influence face recognition, such as face pose, occlusion, illumination, facial expression and image quality. When algorithms produce errors on these datasets, it is not clear which of the factors has caused this error and, hence, there is no guidance in which direction more research is required. This work is a followup from our previous works developed in 2014 and eventually published in 2016, showing the impact of various facial aspects on face recognition algorithms. By comparing the current state-of-the-art with the best systems from the past, we demonstrate that faces under strong occlusions, some types of illumination, and strong expressions are problems mastered by deep learning algorithms, whereas recognition with low-resolution images, extreme pose variations, and open-set recognition is still an open problem. To show this, we run a sequence of experiments using six different datasets and five different face recognition algorithms in an open-source and reproducible manner. We provide the source code to run all of our experiments, which is easily extensible so that utilizing your own deep network in our evaluation is just a few minutes away.

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Join the community, add a new evaluation result row, face recognition.

606 papers with code • 23 benchmarks • 64 datasets

Facial Recognition is the task of making a positive identification of a face in a photo or video image against a pre-existing database of faces. It begins with detection - distinguishing human faces from other objects in the image - and then works on identification of those detected faces.

The state of the art tables for this task are contained mainly in the consistent parts of the task : the face verification and face identification tasks.

( Image credit: Face Verification )

Benchmarks Add a Result

--> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> -->

Trend	Dataset	Best Model	Paper	Code	Compare
		GhostFaceNetV2-1 (MS1MV3)
		GhostFaceNetV2-1
		MS1MV2, R100, SFace
		Fine-tuned ArcFace
		Fine-tuned ArcFace
		ArcFace+CSFM
		PIC - QMagFace
		Prodpoly
		Prodpoly
		PIC - MagFace
		PIC - ArcFace
		FaceNet+Adaptive Threshold
		FaceNet+Adaptive Threshold
		FaceNet+Adaptive Threshold
		Model with Up Convolution + DoG Filter (Aligned)
		Model with Up Convolution + DoG Filter
		GhostFaceNetV2-1
		Model with Up Convolution + DoG Filter
		GhostFaceNetV2-1
		Multi-task
		FaceTransformer+OctupletLoss
		Partial FC
		MCN

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Facenet: a unified embedding for face recognition and clustering.

On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99. 63%.

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

Recently, a popular line of research in face recognition is adopting margins in the well-established softmax loss function to maximize class separability.

VGGFace2: A dataset for recognising faces across pose and age

The dataset was collected with three goals in mind: (i) to have both a large number of identities and also a large number of images for each identity; (ii) to cover a large range of pose, age and ethnicity; and (iii) to minimize the label noise.

SphereFace: Deep Hypersphere Embedding for Face Recognition

This paper addresses deep face recognition (FR) problem under open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space.

A Light CNN for Deep Face Representation with Noisy Labels

This paper presents a Light CNN framework to learn a compact embedding on the large-scale face data with massive noisy labels.

Learning Face Representation from Scratch

The current situation in the field of face recognition is that data is more important than algorithm.

Circle Loss: A Unified Perspective of Pair Similarity Optimization

This paper provides a pair similarity optimization viewpoint on deep feature learning, aiming to maximize the within-class similarity $s_p$ and minimize the between-class similarity $s_n$.

MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition

In this paper, we design a benchmark task and provide the associated datasets for recognizing face images and link them to corresponding entity keys in a knowledge base.

DeepID3: Face Recognition with Very Deep Neural Networks

Very deep neural networks recently achieved great success on general object recognition because of their superb learning capacity.

Can we still avoid automatic face detection?

Recognito-Vision/Linux-FaceRecognition-FaceLivenessDetection • 14 Feb 2016

In this setting, is it still possible for privacy-conscientious users to avoid automatic face detection and recognition?

• DOI: 10.1007/s41095-022-0317-1
• Corpus ID: 260645664

3D face recognition: A comprehensive survey in 2022

• Yaping Jing , Xuequan Lu , Shang Gao
• Published in Computational Visual Media 5 August 2023
• Computer Science

9 Citations

3d face recognition: two decades of progress and prospects, face recognition: a literature review, deep learning-powered face detection and recognition for challenging environments, novel and effective approach for multiview biometric object detection using deep learning based cutting edge techniques, exploration on facial image recognition and processing algorithms under hadoop, mart: masked affective representation learning via masked temporal distribution distillation, an improved machine learning method by applying cloud forensic meta-model to enhance the data collection process in cloud environments, veintr: robust end-to-end full-hand vein identification with transformer, study on the applications of artificial intelligence in image recognition, 171 references, 3d face recognition: a survey, a fast and robust 3d face recognition approach based on deeply learned face representation.

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3-D face recognition: features, databases, algorithms and challenges

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