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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Dissertation
Emotion detection in real-time on an Android Smartphone
TL;DR: An approach of the widely researched topic of automatically detecting human emotions is brought to the Android platform by means of an application in order to detect emotions with few computations so that it may run in real time on smartphones with less computational power.
Compact Convolutional Neural Network Cascade for Face Detection
TL;DR: In this paper, a new solution to the frontal face detection problem based on a compact convolutional neural networks cascade was proposed, which is able to compete with state-of-the-art algorithms.
Book ChapterDOI
CNN Customizations With Transfer Learning for Face Recognition Task
TL;DR: The authors study the use of pretrained models and customizing them towards accuracy and size against face recognition tasks, and show that among the various networks with different data sets, SqueezeNet can achieve the same accuracy as others with small size.
ACWFace: efficient and lightweight face detector based on RetinaFace
TL;DR: A lightweight and efficient single-stage face detector, named ACWFace, which explores the effects of attention, context module, and weighted feature fusion based on RetinaFace, and is designed to further explore the potential of channel attention and spatial attention.
Journal ArticleDOI
Semantic convolutional features for face detection
TL;DR: Wang et al. as mentioned in this paper proposed a novel feature pyramid fashion to produce semantic features at all levels of the network for specially addressing the problem of face detection, where a Semantic Convolutional Box (SCBox) is presented by merging the features from different layers in a bottom-up fashion.
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.