Person-Speci c Characteristic Feature Selection for Face Recognition in .NET

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Person-Speci c Characteristic Feature Selection for Face Recognition
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Figure 5.7. Typical crossing of two parents to create an offspring.
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locations of crossover randomly. As seen in the Figure 5.7, these locations are arbitrary gene boundary locations and at these locations the gene content from the two parents gets mixed. The offspring thus created now contains parts of the genes coming from the contributing parents. The motivation for this step is the fact that, as more and more generations pass, the ttest parents undergoing crossover will already contain the better sets of parameters, and their crossing might bring together the better sets of parameter values from both the parents. Mutation
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In addition to the process of crossover at gene boundaries in the chromosome, the values of some parameters within the genes might be changed randomly. This is illustrated in the Figure 5.8. Such mutations help in exploring the local parameter space more thoroughly. Mutations can be seen as small perturbations to the larger search that explores the vast parameter space, searching for the global minima.
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Most feature-based face recognition methods use feature detectors that are not tailored speci cally for face recognition, and they make no attempt to selectively choose feature detectors based speci cally on their usefulness for face recognition. The method
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5.6 Methodology
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Figure 5.8. Mutation of a newly created offspring.
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described in this paper uses Gabor wavelets as feature detectors, but evaluates the usefulness of each particular feature detector (and a corresponding (x, y) location) for distinguishing between the faces within our face database. Given the very large number of possible Gabor feature detectors and locations, we use a genetic algorithm (GA) to explore the space of possibilities, with a tness function that propagates parents with a higher ability to distinguish between the faces in the database. By selecting the Gabor feature detectors and locations that are most useful for distinguishing each person from all of the other people in the database, we de ne a unique (i.e., person-speci c) feature space for each person.
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The FacePix(30) Database
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All experiments were conducted with face images from the FacePix(30) database [32]. FacePix(30) was compiled to contain face images with pose and illumination angles annotated in 1 increments. Figure 5.9 shows the apparatus that is used for capturing the face images. A video camera and a spotlight are mounted on separate annular rings, which rotate independently around a subject seated in the center. Angle markings on the rings are captured simultaneously with the face image in a video sequence, from which the required frames are extracted. This database has face images of 30 people across a spectrum of pose and illumination angles. For each person in the database, there are three sets of images. (1) The
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Person-Speci c Characteristic Feature Selection for Face Recognition
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Figure 5.9. The data capture setup for FacePix(30).
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pose angle set contains face images of each person at pose angles from +90 to 90 . (2) The no-ambient-light set contains frontal face images with a spotlight placed at angles ranging from +90 to 90 with no ambient light. (3) The ambient-light set contains frontal face images with a spot light placed at angles placed at angels from +90 to 90 in the presence of ambient light. Thus, for each person, there are three face images available for every angle, over a range of 180 . Figure 5.10 provides two examples extracted from the database, showing pose angles and illumination angles ranging from 90 to +90 in steps of 10 . For earlier work using images from this database, please refer to reference 33. Work is currently in progress to make this database publicly available.
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Figure 5.10. Sample face images with varying pose and illumination from the FacePix (30)
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5.6 Methodology
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Figure 5.11. Sample frontal images of one person from the FacePix (30) database.
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We selected at random two images out of each set of three frontal (0 ) (Figure 5.11) images for training, and used the remaining image for testing. The genetic algorithms used the training images to nd a set of Gabor feature detectors that were able to distinguish each person s face from all of the other people in the training set. These feature detectors were then used to recognize the test images. In order to evaluate the performance of our system, we used the same set of training and testing images with face classi cation algorithm based on low-dimensional representation of face images extracted through principal component analysis [34]. Speci cally, the performance of the implementation of PCA-based face recognition followed by reference 35 was used in our experiments.