Abstract
Optical correlation has a rich history in image recognition applications from a database. In practice, it is simple to implement optically using two lenses or numerically using two Fourier transforms. Even if correlation is a reliable method for image recognition, it may jeopardize decision making according to the location, height, and shape of the correlation peak within the correlation plane. Additionally, correlation is very sensitive to image rotation and scale. To overcome these issues, in this study, we propose a method of nonparametric modelling of the correlation plane. Our method is based on a kernel estimation of the regression function used to classify the individual images in the correlation plane. The basic idea is to improve the decision by taking into consideration the energy shape and distribution in the correlation plane. The method relies on the calculation of the Hausdorff distance between the target correlation plane (of the image to recognize) and the correlation planes obtained from the database (the correlation planes computed from the database images). Our method is tested for a face recognition application using the Pointing Head Pose Image Database (PHPID) database. Overall, the results demonstrate good performances of this method compared to competitive methods in terms of good detection and very low false alarm rates.
Highlights
The use of correlation methods [1,2,3,4] remains very competitive despite the abundance of purely numerical methods, such as Support Vector Machines and neural networks
Correlation is easy to use in practice because it is based on two Fourier transforms (FTs) and one multiplication in the frequency domain [5]
We found that when using this training set, a mean square error (MSE) of 4.8% on the whole testing set (541 planes of correlation), corresponding to the first series of the Pointing Head Pose Image Database (PHPID) database, and only one false positive, i.e., a false positive that is an error of prediction when the person must not be recognized, but the person is recognized by the algorithm as person 0, it is a type I error
Summary
The use of correlation methods [1,2,3,4] remains very competitive despite the abundance of purely numerical methods, such as Support Vector Machines and neural networks. The primary focus of this paper is to deal with an authentication problem using a database. Our primary goal is to optimize the solution to verification. Here,2our of 12 problem using primary goal is to optimize the solution to verification. The regression function between the binary output (class of person) person) and the input (correlation plane) is nonparametrically estimated for the learning database by and the input (correlation plane) is nonparametrically estimated for the learning database by making making use of the modified kernel smoothing Nadaraya–Watson algorithm [13,14]. We propose the use of kernel smoothing estimation estimation to cope with the correlation plane, and we choose an appropriate distance, i.e.,to cope the correlation plane, wekernel choose appropriate the Hausdorff distance, the with.
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