An Efficient Method for Face Quality Assessment on the Edge
- URL: http://arxiv.org/abs/2207.09505v1
- Date: Tue, 19 Jul 2022 18:29:43 GMT
- Title: An Efficient Method for Face Quality Assessment on the Edge
- Authors: Sefa Burak Okcu, Burak O\u{g}uz \"Ozkalayc{\i} and Cevahir
\c{C}{\i}\u{g}la
- Abstract summary: A practical approach on edge devices should prioritize these detection of identities according to their conformity to recognition.
We propose a face quality score regression by just appending a single layer to a face landmark detection network.
With almost no additional cost, face quality scores are obtained by training this single layer to regress recognition scores with surveillance like augmentations.
- Score: 1.7188280334580197
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Face recognition applications in practice are composed of two main steps:
face detection and feature extraction. In a sole vision-based solution, the
first step generates multiple detection for a single identity by ingesting a
camera stream. A practical approach on edge devices should prioritize these
detection of identities according to their conformity to recognition. In this
perspective, we propose a face quality score regression by just appending a
single layer to a face landmark detection network. With almost no additional
cost, face quality scores are obtained by training this single layer to regress
recognition scores with surveillance like augmentations. We implemented the
proposed approach on edge GPUs with all face detection pipeline steps,
including detection, tracking, and alignment. Comprehensive experiments show
the proposed approach's efficiency through comparison with SOTA face quality
regression models on different data sets and real-life scenarios.
Related papers
- UniForensics: Face Forgery Detection via General Facial Representation [60.5421627990707]
High-level semantic features are less susceptible to perturbations and not limited to forgery-specific artifacts, thus having stronger generalization.
We introduce UniForensics, a novel deepfake detection framework that leverages a transformer-based video network, with a meta-functional face classification for enriched facial representation.
arXiv Detail & Related papers (2024-07-26T20:51:54Z) - DeepFidelity: Perceptual Forgery Fidelity Assessment for Deepfake
Detection [67.3143177137102]
Deepfake detection refers to detecting artificially generated or edited faces in images or videos.
We propose a novel Deepfake detection framework named DeepFidelity to adaptively distinguish real and fake faces.
arXiv Detail & Related papers (2023-12-07T07:19:45Z) - Exploring Decision-based Black-box Attacks on Face Forgery Detection [53.181920529225906]
Face forgery generation technologies generate vivid faces, which have raised public concerns about security and privacy.
Although face forgery detection has successfully distinguished fake faces, recent studies have demonstrated that face forgery detectors are very vulnerable to adversarial examples.
arXiv Detail & Related papers (2023-10-18T14:49:54Z) - SwinFace: A Multi-task Transformer for Face Recognition, Expression
Recognition, Age Estimation and Attribute Estimation [60.94239810407917]
This paper presents a multi-purpose algorithm for simultaneous face recognition, facial expression recognition, age estimation, and face attribute estimation based on a single Swin Transformer.
To address the conflicts among multiple tasks, a Multi-Level Channel Attention (MLCA) module is integrated into each task-specific analysis.
Experiments show that the proposed model has a better understanding of the face and achieves excellent performance for all tasks.
arXiv Detail & Related papers (2023-08-22T15:38:39Z) - COMICS: End-to-end Bi-grained Contrastive Learning for Multi-face Forgery Detection [56.7599217711363]
Face forgery recognition methods can only process one face at a time.
Most face forgery recognition methods can only process one face at a time.
We propose COMICS, an end-to-end framework for multi-face forgery detection.
arXiv Detail & Related papers (2023-08-03T03:37:13Z) - MMNet: Multi-Collaboration and Multi-Supervision Network for Sequential
Deepfake Detection [81.59191603867586]
Sequential deepfake detection aims to identify forged facial regions with the correct sequence for recovery.
The recovery of forged images requires knowledge of the manipulation model to implement inverse transformations.
We propose Multi-Collaboration and Multi-Supervision Network (MMNet) that handles various spatial scales and sequential permutations in forged face images.
arXiv Detail & Related papers (2023-07-06T02:32:08Z) - Watch Out for the Confusing Faces: Detecting Face Swapping with the
Probability Distribution of Face Identification Models [37.49012763328351]
We propose a novel face swapping detection approach based on face identification probability distributions.
IdP_FSD is specially designed for detecting swapped faces whose identities belong to a finite set.
IdP_FSD exploits face swapping's common nature that the identity of swapped face combines that of two faces involved in swapping.
arXiv Detail & Related papers (2023-03-23T09:33:10Z) - Searching for Alignment in Face Recognition [37.91087888250405]
We first explore and highlight the effects of different alignment templates on face recognition.
Then, for the first time, we try to search for the optimal template automatically.
We construct a well-defined searching space by decomposing the template searching into the crop size and vertical shift.
Experiments on our proposed benchmark validate the effectiveness of our method to improve face recognition performance.
arXiv Detail & Related papers (2021-02-10T14:09:16Z) - SER-FIQ: Unsupervised Estimation of Face Image Quality Based on
Stochastic Embedding Robustness [15.431761867166]
We propose a novel concept to measure face quality based on an arbitrary face recognition model.
We compare our proposed solution on two face embeddings against six state-of-the-art approaches from academia and industry.
arXiv Detail & Related papers (2020-03-20T16:50:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.