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
Journal ArticleDOI
Unconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks
Jun-Cheng Chen,Rajeev Ranjan,Swami Sankaranarayanan,Amit Kumar,Ching-Hui Chen,Vishal M. Patel,Carlos D. Castillo,Rama Chellappa +7 more
TL;DR: In this paper, the authors present the design details of a deep learning system for unconstrained face recognition, including modules for face detection, association, alignment, and face verification.
Proceedings ArticleDOI
Group Sampling for Scale Invariant Face Detection
TL;DR: A group sampling method which divides the anchors into several groups according to the scale, and ensures that the number of samples for each group is the same during training, is proposed, able to advance the state-of-the-arts in face detection.
Journal ArticleDOI
Students’ affective content analysis in smart classroom environment using deep learning techniques
TL;DR: A novel max margin face detection based method for students’ affective content analysis using their facial expressions is proposed and promising results were obtained.
Book ChapterDOI
Unsupervised Hard Example Mining from Videos for Improved Object Detection
SouYoung Jin,Aruni RoyChowdhury,Huaizu Jiang,Ashish Singh,Aditya Prasad,Deep Chakraborty,Erik Learned-Miller +6 more
TL;DR: It is shown how large numbers of hard negatives can be obtained automatically by analyzing the output of a trained detector on video sequences, and how retraining detectors on these automatically obtained examples often significantly improves performance.
Journal ArticleDOI
A fast face detection method via convolutional neural network
TL;DR: Zhang et al. as discussed by the authors proposed a fast face detection method based on discriminative complete features (DCFs) extracted by an elaborately designed convolutional neural network, where face detection is directly performed on the complete feature maps.
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.