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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Journal ArticleDOI
Evaluation of Various State of the Art Head Pose Estimation Algorithms for Clinical Scenarios
TL;DR: Current state-of-the-art head pose algorithms designed to capture the facial geometry from videos, OpenFace 2.0, MediaPipe and 3DDFA_V2, are evaluated and compared and it is suggested that this algorithm can be used in clinical scenarios.
Proceedings ArticleDOI
Towards an Integrated Method of Detection and Description for Face Authentication System
TL;DR: The work in this paper aims to construct a face authentication system based on the deep learning that can be used in a new environment with novel faces without re-training the network.
Book ChapterDOI
YOLO-FD: YOLO for Face Detection
TL;DR: This paper proposes a customized version of the state-of-the-art object detector, YOLOv3, for face detection, aimed at building a real-time, accurate model capable of detecting faces as small as 16 pixels in 34 FPS.
Proceedings ArticleDOI
Evaluation of Anomaly Detection Algorithms for the Real-World Applications
TL;DR: In this article, the authors present an evaluation methodology for anomaly detection in complex data structures that avoids the pitfalls of anomaly detection algorithms in real-world problems such as quality control in modern manufacturing, where the anomalous samples are usually rare, resulting in highly unbalanced datasets.
Journal ArticleDOI
2D Hand Detection Using Multi-Feature Skin Model Supervised Cascaded CNN
TL;DR: A new method for 2D hand detection which can be considered as a combination of Multi-Feature based hand proposal generation and cascaded convolutional neural network (CCNN) classification is proposed.
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.