Proceedings ArticleDOI
Rotation invariant neural network-based face detection
Henry Allan Rowley,Shumeet Baluja,Takeo Kanade +2 more
- pp 963-963
Reads0
Chats0
TLDR
This paper presents a neural network-based face detection system, which is limited to detecting upright, frontal faces, and presents preliminary results for detecting faces rotated out of the image plane, such as profiles and semi-profiles.Abstract:
In this paper, we present a neural network-based face detection system. Unlike similar systems which are limited to detecting upright, frontal faces, this system detects faces at any degree of rotation in the image plane. The system employs multiple networks; a "router" network first processes each input window to determine its orientation and then uses this information to prepare the window for one or more "detector" networks. We present the training methods for both types of networks. We also perform sensitivity analysis on the networks, and present empirical results on a large test set. Finally, we present preliminary results for detecting faces rotated out of the image plane, such as profiles and semi-profiles.read more
Citations
More filters
Journal ArticleDOI
From few to many: illumination cone models for face recognition under variable lighting and pose
TL;DR: A generative appearance-based method for recognizing human faces under variation in lighting and viewpoint that exploits the fact that the set of images of an object in fixed pose but under all possible illumination conditions, is a convex cone in the space of images.
Book
Computer Vision: Algorithms and Applications
TL;DR: Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images and takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene.
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.
Journal ArticleDOI
Face detection in color images
TL;DR: A face detection algorithm for color images in the presence of varying lighting conditions as well as complex backgrounds is proposedBased on a novel lighting compensation technique and a nonlinear color transformation, this method detects skin regions over the entire image and generates face candidates based on the spatial arrangement of these skin patches.
Journal ArticleDOI
Face Detection
Erik Hjelmås,Boon Low +1 more
TL;DR: A comprehensive and critical survey of face detection algorithms, ranging from simple edge-based algorithms to composite high-level approaches utilizing advanced pattern recognition methods, is presented.
References
More filters
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.
Proceedings ArticleDOI
Training support vector machines: an application to face detection
TL;DR: A decomposition algorithm that guarantees global optimality, and can be used to train SVM's over very large data sets is presented, and the feasibility of the approach on a face detection problem that involves a data set of 50,000 data points is demonstrated.
Proceedings ArticleDOI
View-based and modular eigenspaces for face recognition
Pentland,Moghaddam,Starner +2 more
TL;DR: In this paper, a view-based multiple-observer eigenspace technique is proposed for use in face recognition under variable pose, which incorporates salient features such as the eyes, nose and mouth, in an eigen feature layer.
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
TL;DR: This paper presents an empirical analysis of where the proposed technique will outperform genetic algorithms, and describes a class of problems in which a genetic algorithm may be able to perform better.
Journal ArticleDOI
Human face detection in a complex background
Guangzheng Yang,Thomas S. Huang +1 more
TL;DR: The problem of scale is dealt with, so that the system can locate unknown human faces spanning a wide range of sizes in a complex black-and-white picture.