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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About Face: A Survey of Facial Recognition Evaluation.
TL;DR: Over 100 face datasets constructed between 1976 to 2019 of 145 million images of over 17 million subjects from a range of sources, demographics and conditions reveal that these datasets are contextually informed shaped by changes in political motivations, technological capability and current norms.
Proceedings ArticleDOI
Thermal image-based CNN's for ultra-low power people recognition
TL;DR: This paper developed a people counting algorithm on thermal images based on convolutional neural networks small enough that they can run on a limited-memory low-power platform and developed a fast and accurate CNN that is able to provide a completely error-free detection on 53.7% of the test images.
Posted Content
WIDER FACE: A Face Detection Benchmark
TL;DR: WIDER FACE as discussed by the authors is a large-scale face detection dataset with rich annotations including occlusions, poses, event categories, and face bounding boxes, which can be used for training face detection models.
Journal ArticleDOI
Fast cascade face detection with pyramid network
TL;DR: This paper proposes a fast CNNs cascade face detector with multi-task learning and network acceleration techniques, which is comparable in performance with the-state-of-arts while the speed reaches astonishing 165 fps on Titan GPU.
Book ChapterDOI
Detecting Driver Drowsiness in Real Time Through Deep Learning Based Object Detection
Muhammad Shakeel,Nabit A. Bajwa,Ahmad Muhammad Anwaar,Anabia Sohail,Asifullah Khan,Haroon-ur-Rashid +5 more
TL;DR: A novel deep learning methodology based on Convolutional Neural Networks (CNN) to tackle drowsiness detection in drivers, which can process an incoming video stream in real time on a standalone mobile device without the need of expensive hardware support.
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.