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Join the community, add a new evaluation result row, face recognition.
557 papers with code • 22 benchmarks • 61 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
Most implemented papers
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?
In this setting, is it still possible for privacy-conscientious users to avoid automatic face detection and recognition?
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recognition, using a linear projection onto a low dimension subspace. In contrast to the. Eigen-face, which maximizes the total variance within classes across all faces, the Fisher-. face approach confines the variance within classes to the classes themselves.
Abstract and Figures. The task of face recognition has been actively researched in recent years. This paper provides an up-to-date review of major human face recognition research. We first present ...
Abstract and Figures. Face recognition technology is a biometric technology, which is based on the identification of facial features of a person. People collect the face images, and the ...
A face recognition system is designed, implemented and tested in this thesis study. The system utilizes a combination of techniques in two topics; face detection and recognition.
facial recognition is known as the Karhunen-Loeve method. It is the most thoroughly studied. method for face recognition, with its main usability being a reduction in the dimensionality of the image. This method was first applied for face recognition and then subsequently used for facial. reconstruction.
Face recognition is one of the most active research fields of computer vision and pattern recognition, with many practical and commercial applications including identification, access control, forensics, and human-computer interactions. However, identifying a face in a crowd raises serious questions about individual freedoms and poses ethical issues. Significant methods, algorithms, approaches ...
To deal with the issue of human face recognition on small original dataset, a new approach combining convolutional neural network (CNN) with augmented dataset is developed in this paper. The original small dataset is augmented to be a large dataset via several transformations of the face images. Based on the augmented face image dataset, the ...
face recognition relate to occlusion, illumination and pose invariance, which causes a notable decline in accuracy in both traditional handcrafted solutions and deep neural networks. This survey will provide a critical analysis and comparison of modern state of the art methodologies, their benefits, and their limitations. It provides a ...
rotation. Therefore, face recognition based on deep learning can greatly improve the recognition speed and compatible external interference. In this thesis, we use convolutional neural networks (ConvNets) for face recognition, the neural networks have the merits of end-to-end, sparse connection and weight sharing.
Face Recognition is the process of identifying and verifying the faces. Face recognition has vast importance in the field of Security, Healthcare, Banking, Criminal Identification, Payment, and Advertising. In this paper, we have reviewed various techniques and challenges for the face recognition. Illumination, pose variation, facial ...
Goal of this master thesis Developing a face recognition system so that: Keeps a DB of known users Given a new picture, determines the closest match Capable of on-line learning Usable in uncontrolled environments Reasonably fast Barcelona School of Informatics Face recognition using Deep Learning 7 / 44. Outline
Face recognition has attracted particular interest in biometric recognition with wide applications in security, entertainment, health, marketing. ... This thesis contributes towards in the wild face recognition from three perspectives including network design, model compression, and model explanation. Firstly, although the facial land-
This thesis presents a comparison of traditional and deep learning face recognition algorithms under the presence of noise. For this purpose, Gaussian and salt-and-pepper noises are applied to the face images drawn from the ORL Dataset. The image recognition is performed using each of the following eight algorithms: princi-
557 papers with code • 22 benchmarks • 61 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.
Thesis. Full-text available. Jun 2022; Isanka Diddeniya; ... Face recognition is a growing technology that has been broadly employed in forensics applications such as unlawful person ...
The work of this thesis aims at designing a robust Facial Expression Recognition (FER) system by combining various techniques from computer vision and pattern recognition. Expression recognition is closely related to face recognition where a lot of research has been done and a vast array of algorithms have been introduced. FER can also be
uliege.be
This thesis project aimed to build a facial recognition system that could recognize people through the camera and unlock the door locks. Recognized results were sent to the database and could be analyzed by users after the successful login. The project consists of building a facial recognition, electronics operation, and
Montclair State University Digital Commons
Thesis for: Master of Science; Advisor: Ausif Mahmood; Authors: ... Application of face recognition has been implemented using the pre trained model Facenet and Deep Convolutional Neural Networks ...
1.2 Face Recognition: With the information vector in hand, the next and final step would be to recognize the faces. Face recognition step takes in the feature vectors as input and identifies the faces. To achieve this, a face database is built with multiple images of persons. These images are used to extract features of the person.
algorithm.Once the system is trained, it can recognize the faces of authorized students in real-time. When a student's. face is detected by the camera, the system matches the detected face with ...