Open Access
FDDB: A benchmark for face detection in unconstrained settings
Vidit Jain,Erik Learned-Miller +1 more
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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
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Proceedings ArticleDOI
Convolutional Neural Networks for Visual Information Analysis with Limited Computing Resources
TL;DR: The behavior of various model configurations in object detection tasks are investigated and a comparative study on inference optimization methods which aim to reduce the computational cost of Convolutional Neural Networks are performed, while examining the effect of such methods on their performance, and proposing architecture modifications for this purpose.
Proceedings ArticleDOI
Deep Face Detector Adaptation Without Negative Transfer or Catastrophic Forgetting
TL;DR: This work proposes a novel face detector adaptation approach that works as long as there are representative images of the target domain no matter they are labeled or not and, more importantly, without the need of accessing the training data of the source domain.
Journal ArticleDOI
A lightweight face detector by integrating the convolutional neural network with the image pyramid
TL;DR: A single-stage face detector with an extremely lightweight CNN to achieve fast and accurate detection and the outstanding detection performance and lightweight model size signify its effectiveness and practicability.
Proceedings ArticleDOI
Adversarial Occlusion-aware Face Detection
TL;DR: In this article, an adversarial training strategy is employed to generate occlusion-like face features that are difficult for a face detector to recognize, and the supervisory signals from the segmentation branch will reversely affect the features, helping extract more informative features.
Posted Content
A Method for Object Detection Based on Pixel Intensity Comparisons Organized in Decision Trees
TL;DR: In this paper, an ensemble of optimized decision trees organized in a cascade of rejectors is proposed for visual object detection based on pixel intensity comparisons in their internal nodes and this makes them able to process image regions very fast.
References
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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.