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
Citations
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Proceedings ArticleDOI
A machine learning approach to detect occluded faces in unconstrained crowd scene
Shazia Gul,Humera Farooq +1 more
TL;DR: This work is an attempt to illustrate the cognitive informatics approach using machine learning and present an occluded face detection method that achieves desirable results in the detection of half occluding faces.
Book ChapterDOI
Face Detection for Crowd Analysis Using Deep Convolutional Neural Networks
TL;DR: Results show that when images contain fair sized occlusions, Mask RCNN outperforms the current state of the art method.
Journal ArticleDOI
Autonomous robot photographer with KL divergence optimization of image composition and human facial direction
Kai Lan,Kosuke Sekiyama +1 more
TL;DR: Two technical issues of scene composition evaluation and viewpoint selection are solved by this robot photography system and the fact that better composed photos can be autonomously photographed by the proposed system is validated via experiments and human evaluations.
Proceedings ArticleDOI
Low-complexity object detection with deep convolutional neural network for embedded systems
TL;DR: An end-to-end TensorFlow (TF)-based fully-convolutional deep neural network for generic object detection task inspired by one of the fastest framework, YOLO, which can predict object instances of different sizes and poses in a single frame and achieves comparative accuracy compared with state-of-the-art CNN-based object detection methods.
Proceedings ArticleDOI
Multi-path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained "Hard Faces"
Yuguang Liu,Martin D. Levine +1 more
TL;DR: Experiments show that this approach achieves state-of-the-art face detection performance on the WIDER FACE dataset "hard" partition, outperforming the former best result by 9.6% for the Average Precision.
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.