Genetic search for face detection and verification

Bebis, George; Uthiram, Satish; Georgiopoulos, Michael

doi:10.1109/iciis.1999.810289

Cited by 7 publications

(1 citation statement)

References 22 publications

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“…Here, we employ a more powerful fitness function based on the theory of eigenspaces. An earlier version of this work appears in 24 The rest of the paper is organized as follows: In Section 2, we provide brief review of the eigenface approach while Section 3 contains an introduction to genetic algorithms. Section 4 presents the proposed approach in detail including the preprocessing steps, eigenspace representation, encoding, and fitness evaluation.…”

Section: Introductionmentioning

confidence: 99%

Face Detection and Verification Using Genetic Search

Bebis

Uthiram

Georgiopoulos

2000

Int. J. Artif. Intell. Tools

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We consider the problem of searching for the face of a particular individual in a twodimensional intensity image. This problem has many potential applications such as locating a person in a crowd using images obtained by surveillance cameras. There are two steps in solving this problem: first, face regions must be extracted from the image(s) (face detection) and second, candidate faces must be compared against the face of interest (face verification). Without any a-priori knowledge about the location and size of a face in an image, every possible image location and face size should be considered, leading to a very large search space. In this paper, we propose using Genetic Algorithms (GAs) for searching the image efficiently. Specifically, we use GAs to find image sub-windows that contain faces and in particular, the face of interest. Each sub-window is evaluated using a fitness function containing two terms: the first term favors sub-windows containing faces while the second term favors sub-windows containing faces similar to the face of interest. Both terms have been derived using the theory of eigenspaces. A set of increasingly complex scenes demonstrate the performance of the proposed genetic-search approach.

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Section: Introductionmentioning

confidence: 99%

Face Detection and Verification Using Genetic Search

Bebis

Uthiram

Georgiopoulos

2000

Int. J. Artif. Intell. Tools

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Comparison of Optimisation Algorithms for Deformable Template Matching

Zografos

2009

Advances in Visual Computing

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Genetic object recognition using combinations of views

Bebis

Louis

Varol

et al. 2002

IEEE Trans. Evol. Computat.

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We investigate the application of genetic algorithms (GAs) for recognizing real two-dimensional (2-D) or three-dimensional (3-D) objects from 2-D intensity images, assuming that the viewpoint is arbitrary. Our approach is model-based (i.e., we assume a predefined set of models), while our recognition strategy lies on the recently proposed theory of algebraic functions of views. According to this theory, the variety of 2-D views depicting an object can be expressed as a combination of a small number of 2-D views of the object. This implies a simple and powerful strategy for object recognition: novel 2-D views of an object (2-D or 3-D) can be recognized by simply matching them to combinations of known 2-D views of the object. In other words, objects in a scene are recognized by "predicting" their appearance through the combination of known views of the objects. This is an important idea, which is also supported by psychophysical findings indicating that the human visual system works in a similar way. The main difficulty in implementing this idea is determining the parameters of the combination of views. This problem can be solved either in the space of feature matches among the views ("image space") or the space of parameters ("transformation space"). In general, both of these spaces are very large, making the search very time consuming. In this paper, we propose using GAs to search these spaces efficiently. To improve the efficiency of genetic search in the transformation space, we use singular value decomposition and interval arithmetic to restrict genetic search in the most feasible regions of the transformation space. The effectiveness of the GA approaches is shown on a set of increasingly complex real scenes where exact and near-exact matches are found reliably and quickly.

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Genetic search for face detection and verification

Cited by 7 publications

References 22 publications

Face Detection and Verification Using Genetic Search

Face Detection and Verification Using Genetic Search

Comparison of Optimisation Algorithms for Deformable Template Matching

Genetic object recognition using combinations of views

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