A Face Categorization Algorithm Based on Convolutional Neural Networks and Principal Component Analysis

Abstract

The study objective is face recognition for identification purposes. The input data to be classified are attribute vectors generated by a deep learning neural network. The few existing algorithms can perform sufficiently reliable openset classification.

The common approach to classification is using a classification threshold. It has several disadvantages leading to the low quality of openset classifications. The key disadvantages are as follows. First, there is no set threshold: it is impossible to find a common threshold suitable for every face. Second, the higher the threshold, the lower the quality of classification. Third, with the threshold classification more than one class can match a face.

For this reason, we proposed to apply the principal component analysis as an extra dimensionality reduction tool besides identifying the key face attributes by a deep learning neural network for subsequent classification of the attribute vectors. In geometric terms, the principal component analysis application to attribute vectors with subsequent classification is similar to a search for a lowdimension space where the projections of the source vectors can be easily separated. The dimensionality reduction concept is based on the assumption that not all the components on Ndimensional attribute vectors are relevant for the human face representation, and only some of them produce the larger part of the dispersion. Therefore, by selecting only the relevant components of the attribute vectors we can separate the classes using the most variable attributes while skipping the less informative data and not comparing the vectors in a highdimensional space.