ROTATED SPHERE HAAR WAVELET AND DEEP CONTRACTIVE AUTO-ENCODER NETWORK WITH FUZZY GAUSSIAN SVM FOR PILOT’S PUPIL CENTER DETECTION

Abstract

How to track the attention of the pilot is a huge challenge. We are able to capture the pupil status of the pilot and analyze their anomalies and judge the attention of the pilot. This paper proposes a new approach to solve this problem through the integration of spherical Haar wavelet transform and deep learning methods. First, considering the application limitations of Haar wavelet and other wavelets in spherical signal decomposition and reconstruction, a feature learning method based on the spherical Haar wavelet is proposed. In order to obtain the salient features of the spherical signal, a rotating spherical Haar wavelet is also proposed, which has a consistent scale in the same direction between the reconstructed image and the original image. Second, in order to find a better characteristic representation of the spherical signal, a higher contractive autoencoder (HCAE) is designed for the potential representation of the spherical Haar wavelet coefficients, which has two penalty items, respectively, from Jacobian and two order items from Taylor expansion of the point x for the contract learning of sample space. Third, in order to improve the classification performance, this paper proposes a fuzzy Gaussian support vector machine (FGSVM) as the top classification tool of the deep learning model, which can punish some Gaussian noise from the output of the deep HCAE network (DHCAEN). Finally, a DHCAEN-FGSVM classifier is proposed to identify the location of the pupil center. The experimental results of the public data set and actual data show that our model is an effective method for spherical signal detection.

Existing System

Semiorthogonal, symmetric spherical Haar wavelet basis (SHWB), Boltzmann machine and the autoencoder (AE).

Proposed System

Then, the proposed detection method includes three steps. First, we use the SOHO wavelet transform to get the wavelet coefficients. Second, we use DHCAEN to learn the appropriate features from the coefficients. Finally, FGSVM is used to distinguish between the targets within a spherical signal.  

Architecture

BLOCK DIAGRAM