Periocular authentication in smartphones applying uLBP descriptor on CNN Feature Maps

Abstract

The outputs of CNN layers, called Activations, are composed of Feature Maps, which show textural information that can be extracted by a texture descriptor. Standard CNN feature extraction use Activations as feature vectors for object recognition. The goal of this work is to evaluate a new methodology of CNN feature extraction. In this paper, instead of using the Activations as a feature vector, we use a CNN as a feature extractor, and then we apply a texture descriptor directly on the Feature Maps. Thus, we use the extracted features obtained by the texture descriptor as a feature vector for authentication. To evaluate our proposed method, we use the AlexNet CNN previously trained on the ImageNet database as a feature extractor; then we apply the uniform LBP (uLBP) descriptor on the Feature Maps for texture extraction. We tested our proposed method on the VISOB dataset composed of periocular images taken from 3 different smartphones under 3 different lighting conditions. Our results show that the use of a texture descriptor on CNN Feature Maps achieves better performance than computer vision handcrafted methods or even by standard CNN feature extraction.