Periocular authentication in smartphones applying uLBP descriptor on CNN Feature Maps
William Barcellos, Adilson Gonzaga · 2021
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.