Open Access
FDDB: A benchmark for face detection in unconstrained settings
Vidit Jain,Erik Learned-Miller +1 more
Reads0
Chats0
TLDR
A new data set of face images with more faces and more accurate annotations for face regions than in previous data sets is presented and two rigorous and precise methods for evaluating the performance of face detection algorithms are proposed.Abstract:
Despite the maturity of face detection research, it remains difficult to compare different algorithms for face detection. This is partly due to the lack of common evaluation schemes. Also, existing data sets for evaluating face detection algorithms do not capture some aspects of face appearances that are manifested in real-world scenarios. In this work, we address both of these issues. We present a new data set of face images with more faces and more accurate annotations for face regions than in previous data sets. We also propose two rigorous and precise methods for evaluating the performance of face detection algorithms. We report results of several standard algorithms on the new benchmark.read more
Citations
More filters
Proceedings ArticleDOI
A Comparison of CNN-based Face and Head Detectors for Real-Time Video Surveillance Applications
TL;DR: Comparing the accuracy and complexity of state-of-the-art CNN architectures suitable for face and head detection suggests that, although CNN architectures can achieve a very high level of accuracy compared to traditional detectors, their computational cost can represent a limitation for many practical real-time applications.
Book ChapterDOI
Detector-in-Detector: Multi-level Analysis for Human-Parts
TL;DR: A new framework to boost up the detection performance of the multi-level objects with region-based object detection structure with two carefully designed detectors to separately pay attention to the human body and body parts in a coarse-to-fine manner, which is called Detector-in-Detector network (DID-Net).
Proceedings ArticleDOI
A Driver Fatigue Recognition Algorithm Based on Spatio-Temporal Feature Sequence
TL;DR: Wang et al. as discussed by the authors designed a real-time fatigue state recognition algorithm based on spatio-temporal feature sequence, which can be mainly applied to the scene of fatigue driving recognition.
Proceedings ArticleDOI
Face Detection in Camera Captured Images of Identity Documents Under Challenging Conditions
TL;DR: Three state-of-the-art face detection methods based on general images, i.e. Cascade-CNN, MTCNN and PCN, are surveyed for face detection in camera captured images of identity documents, given different image quality assessments to show the performance and the limitations of the current methods.
Book ChapterDOI
Hierarchical Convolutional Neural Network for Face Detection
TL;DR: The proposed approach of hierarchical convolutional neural network for face detection reaches the state-of-the-art performance and is evaluated on the AFW dataset, FDDB dataset and Pascal Faces, and it has effective generalization.
References
More filters
Journal ArticleDOI
Robust Real-Time Face Detection
Paul A. Viola,Michael Jones +1 more
TL;DR: In this paper, a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates is described. But the detection performance is limited to 15 frames per second.
Journal ArticleDOI
The Hungarian method for the assignment problem
TL;DR: This paper has always been one of my favorite children, combining as it does elements of the duality of linear programming and combinatorial tools from graph theory, and it may be of some interest to tell the story of its origin this article.
Proceedings Article
On Spectral Clustering: Analysis and an algorithm
TL;DR: A simple spectral clustering algorithm that can be implemented using a few lines of Matlab is presented, and tools from matrix perturbation theory are used to analyze the algorithm, and give conditions under which it can be expected to do well.
Journal ArticleDOI
Neural network-based face detection
TL;DR: A neural network-based upright frontal face detection system that arbitrates between multiple networks to improve performance over a single network, and a straightforward procedure for aligning positive face examples for training.
Journal ArticleDOI
Detecting faces in images: a survey
TL;DR: In this article, the authors categorize and evaluate face detection algorithms and discuss relevant issues such as data collection, evaluation metrics and benchmarking, and conclude with several promising directions for future research.