Combined Features for Face Recognition in Surveillance Conditions

Abstract

This paper addresses the challenging problem of face recognition in surveillance conditions based on the recently published database called SCface. This database emphasizes the challenges of face recognition in uncontrolled indoor conditions. In this database, 4160 face images were captured using five different commercial cameras of low resolution, at three different distances, both lighting conditions and face pose were uncontrolled. Moreover, some of the images were taken under night vision mode. This paper introduces a novel feature extraction scheme that combines parameters extracted from both spatial and frequency domains. These features will be referred to as Spatial and Frequency Domains Combined Features (SFDCF). The spatial domain features are extracted using Spatial Deferential Operators (SDO), while the frequency domain features are extracted using Discrete Cosine Transform (DCT). Principal Component Analysis (PCA) is used to reduce the dimensionality of the spatial domain features while zonal coding is used for reducing the dimensionality of the frequency domain features. The two feature sets were simply combined by concatenation to form a feature vector representing the face image. In this paper we provide a comparison, in terms of recognition results, between the proposed features and other typical features; namely, eigenfaces, discrete cosine coefficients, wavelet subband energies, and Gray Level Concurrence Matrix (GLCM) coefficients. The comparison shows that the proposed SFDCF feature set yields superior recognition rates, especially for images captured at far distances and images captured in the dark. The recognition rates using SFDCF reach 99.23% for images captured by different cameras at the same distance. While for images captured at different distances, SFDCF reaches a recognition rate of 93.8%.