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307
Neural NetworkBased Face Detection
 IEEE Transactions On Pattern Analysis and Machine intelligence
, 1998
"... Abstract—We present a neural networkbased upright frontal face detection system. A retinally connected neural network examines small windows of an image and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We ..."
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Cited by 975 (24 self)
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Abstract—We present a neural networkbased upright frontal face detection system. A retinally connected neural network examines small windows of an image and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We present a straightforward procedure for aligning positive face examples for training. To collect negative examples, we use a bootstrap algorithm, which adds false detections into the training set as training progresses. This eliminates the difficult task of manually selecting nonface training examples, which must be chosen to span the entire space of nonface images. Simple heuristics, such as using the fact that faces rarely overlap in images, can further improve the accuracy. Comparisons with several other stateoftheart face detection systems are presented, showing that our system has comparable performance in terms of detection and falsepositive rates. Index Terms—Face detection, pattern recognition, computer vision, artificial neural networks, machine learning.
Multiobjective Evolutionary Algorithms: Analyzing the StateoftheArt
, 2000
"... Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mideighties in an attempt to stochastically solve problems of this generic class. During the past decade, ..."
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Cited by 292 (7 self)
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Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mideighties in an attempt to stochastically solve problems of this generic class. During the past decade, a variety of multiobjective EA (MOEA) techniques have been proposed and applied to many scientific and engineering applications. Our discussion's intent is to rigorously define multiobjective optimization problems and certain related concepts, present an MOEA classification scheme, and evaluate the variety of contemporary MOEAs. Current MOEA theoretical developments are evaluated; specific topics addressed include fitness functions, Pareto ranking, niching, fitness sharing, mating restriction, and secondary populations. Since the development and application of MOEAs is a dynamic and rapidly growing activity, we focus on key analytical insights based upon critical MOEA evaluation of c...
A Survey of Optimization by Building and Using Probabilistic Models
 COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
, 1999
"... This paper summarizes the research on populationbased probabilistic search algorithms based on modeling promising solutions by estimating their probability distribution and using the constructed model to guide the further exploration of the search space. It settles the algorithms in the field of ge ..."
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Cited by 284 (84 self)
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This paper summarizes the research on populationbased probabilistic search algorithms based on modeling promising solutions by estimating their probability distribution and using the constructed model to guide the further exploration of the search space. It settles the algorithms in the field of genetic and evolutionary computation where they have been originated. All methods are classified into a few classes according to the complexity of the class of models they use. Algorithms from each of these classes are briefly described and their strengths and weaknesses are discussed.
Hierarchical Bayesian Optimization Algorithm = Bayesian Optimization Algorithm + Niching + Local Structures
, 2001
"... The paper describes the hierarchical Bayesian optimization algorithm which combines the Bayesian optimization algorithm, local structures in Bayesian networks, and a powerful niching technique. The proposed algorithm is able to solve hierarchical traps and other difficult problems very efficiently. ..."
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Cited by 259 (65 self)
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The paper describes the hierarchical Bayesian optimization algorithm which combines the Bayesian optimization algorithm, local structures in Bayesian networks, and a powerful niching technique. The proposed algorithm is able to solve hierarchical traps and other difficult problems very efficiently.
Designing Efficient And Accurate Parallel Genetic Algorithms
, 1999
"... Parallel implementations of genetic algorithms (GAs) are common, and, in most cases, they succeed to reduce the time required to find acceptable solutions. However, the effect of the parameters of parallel GAs on the quality of their search and on their efficiency are not well understood. This insuf ..."
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Cited by 231 (5 self)
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Parallel implementations of genetic algorithms (GAs) are common, and, in most cases, they succeed to reduce the time required to find acceptable solutions. However, the effect of the parameters of parallel GAs on the quality of their search and on their efficiency are not well understood. This insufficient knowledge limits our ability to design fast and accurate parallel GAs that reach the desired solutions in the shortest time possible. The goal of this dissertation is to advance the understanding of parallel GAs and to provide rational guidelines for their design. The research reported here considered three major types of parallel GAs: simple masterslave algorithms with one population, more sophisticated algorithms with multiple populations, and a hierarchical combination of the first two types. The investigation formulated simple models that predict accurately the quality of the solutions with different parameter settings. The quality predictors were transformed into populationsizing equations, which in turn were used to estimate the execution time of the algorithms.
Linkage Learning via Probabilistic Modeling in the ECGA
, 1999
"... The goal of linkage learning, or building block identification, is the creation of a more effective genetic algorithm (GA). This paper explores the relationship between the linkagelearning problem and that of learning probability distributions over multivariate spaces. Herein, it is argued that th ..."
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Cited by 197 (4 self)
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The goal of linkage learning, or building block identification, is the creation of a more effective genetic algorithm (GA). This paper explores the relationship between the linkagelearning problem and that of learning probability distributions over multivariate spaces. Herein, it is argued that these problems are equivalent. Using a simple but effective approach to learning distributions, and by implication linkage, this paper reveals the existence of GAlike algorithms that are potentially orders of magnitude faster and more accurate than the simple GA. I. Introduction Linkage learning in genetic algorithms (GAs) is the identification of building blocks to be conserved under crossover. Theoretical studies have shown that if an effective linkagelearning GA were developed, it would hold significant advantages over the simple GA (2). Therefore, the task of developing such an algorithm has drawn significant attention. Past approaches to developing such an algorithm have focused on ev...
Rotation invariant neural networkbased face detection
, 1998
"... In this paper, we present a neural networkbased 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 ..."
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Cited by 179 (3 self)
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In this paper, we present a neural networkbased 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 semiprofiles. 1.
Removing The Genetics from The Standard Genetic Algorithm
 In Proceedings of ICML’95
, 1995
"... We present an abstraction of the genetic algorithm (GA), termed populationbased incremental learning (PBIL), that explicitly maintains the statistics contained in a GA’s population, but which abstracts away the crossover operator and redefines the role of the population. This results in PBIL being ..."
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Cited by 179 (12 self)
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We present an abstraction of the genetic algorithm (GA), termed populationbased incremental learning (PBIL), that explicitly maintains the statistics contained in a GA’s population, but which abstracts away the crossover operator and redefines the role of the population. This results in PBIL being simpler, both computationally and theoretically, than the GA. Empirical results reported elsewhere show that PBIL is faster and more effective than the GA on a large set of commonly used benchmark problems. Here we present results on a problem custom designed to benefit both from the GA’s crossover operator and from its use of a population. The results show that PBIL performs as well as, or better than, GAs carefully tuned to do well on this problem. This suggests that even on problems custom designed for GAs, much of the power of the GA may derive from the statistics maintained implicitly in its population, and not from the population itself nor from the crossover operator.
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
 ACM COMPUTING SURVEYS
, 2003
"... The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important meta ..."
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Cited by 176 (14 self)
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The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behaviour of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization.
Human Face Detection in Visual Scenes
, 1995
"... We present a neural networkbased face detection system. A retinally connected neural network examines small windows of an image, and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We use a bootstrap algorith ..."
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Cited by 160 (6 self)
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We present a neural networkbased face detection system. A retinally connected neural network examines small windows of an image, and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We use a bootstrap algorithm for training the networks, which adds false detections into the training set as training progresses. This eliminates the difficult task of manually selecting nonface training examples, which must be chosen to span the entire space of nonface images. Comparisons with other stateoftheart face detection systems are presented; our system has better performance in terms of detection and falsepositive rates. This work was partially supported by a grant from Siemens Corporate Research, Inc., by the Department of the Army, Army Research Office under grant number DAAH0494G0006, and by the Office of Naval Research under grant number N000149510591. This work was started while Shumeet Balu...