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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Book ChapterDOI
Cascaded Random Forest for Fast Object Detection
TL;DR: A Random Forest framework which incorporates a cascade structure consisting of several stages together with a bootstrap approach is proposed, leading to a massively speeded-up detection framework.
Posted Content
Can you find a face in a HEVC bitstream
TL;DR: This work shows how to localize faces in HEVC-coded images, without full reconstruction, and demonstrates the benefits that such approach can have in privacy-friendly face localization.
Journal Article
Race classification using gaussian-based weight K-nn algorithm for face recognition
TL;DR: The race classification scheme proposed which is Gaussian based-weighted K-Nearest Neighbor classifier in this paper, has very sensitive to illumination intensity and the main idea is first to identify the minority class instances in the training data and then generalize them to Gaussian function as concept for the minorities class.
Dissertation
Wearable-assisted social interaction as assistive technology for the blind
TL;DR: This work presents an end-to-end wearable system designed to learn and assist its (potentially blind) wearers with daily social interactions, which visually identifies nearby acquaintances and provides timely, discreet notifications of their presence to the wearer.
Journal ArticleDOI
Face Detection for Privacy Protected Images
TL;DR: This paper proposes a scheme to extract secure Haar feature for privacy-preserving images, which can be applied to the face detection task and shows that the scheme can be implemented in polynomial time.
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.