Face Recognition
549 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 )
Libraries
Use these libraries to find Face Recognition models and implementationsDatasets
Subtasks
Latest papers with no code
Explainable Face Verification via Feature-Guided Gradient Backpropagation
Recent years have witnessed significant advancement in face recognition (FR) techniques, with their applications widely spread in people's lives and security-sensitive areas.
ChatGPT and biometrics: an assessment of face recognition, gender detection, and age estimation capabilities
This paper explores the application of large language models (LLMs), like ChatGPT, for biometric tasks.
Ef-QuantFace: Streamlined Face Recognition with Small Data and Low-Bit Precision
Moreover, we incorporate an evaluation-based metric loss and achieve an outstanding 96. 15% accuracy on the IJB-C dataset, establishing a new state-of-the-art compressed model training for face recognition.
Improving the JPEG-resistance of Adversarial Attacks on Face Recognition by Interpolation Smoothing
JPEG compression can significantly impair the performance of adversarial face examples, which previous adversarial attacks on face recognition (FR) have not adequately addressed.
Quadruplet Loss For Improving the Robustness to Face Morphing Attacks
Recent advancements in deep learning have revolutionized technology and security measures, necessitating robust identification methods.
Mask-up: Investigating Biases in Face Re-identification for Masked Faces
Three of the commercial and five of the open-source FRSs are highly inaccurate; they further perpetuate biases against non-White individuals, with the lowest accuracy being 0%.
Is my Data in your AI Model? Membership Inference Test with Application to Face Images
This paper introduces the Membership Inference Test (MINT), a novel approach that aims to empirically assess if specific data was used during the training of Artificial Intelligence (AI) models.
Only My Model On My Data: A Privacy Preserving Approach Protecting one Model and Deceiving Unauthorized Black-Box Models
The datasets employed are ImageNet, for image classification, Celeba-HQ dataset, for identity classification, and AffectNet, for emotion classification.
Trade-off Between Spatial and Angular Resolution in Facial Recognition
Ensuring robustness in face recognition systems across various challenging conditions is crucial for their versatility.
Efficient Expression Neutrality Estimation with Application to Face Recognition Utility Prediction
The recognition performance of biometric systems strongly depends on the quality of the compared biometric samples.