The image classification is a classical problem of image processing, computer vision and machine learning fields. In this paper we study the image classification using deep learning. We use AlexNet architecture with convolutional neural networks for this purpose. Four test images are selected from the ImageNet database for the classification purpose. We cropped the images for various portion areas and conducted experiments. The results show the effectiveness of deep learning based image classification using AlexNet.
This present paper aims to extract robust dynamic features used to spoofing detection and countermeasure in ASV system. ASV is a biometric person authentication system. Researchers are aiming to develop spoofing detection and countermeasure techniques to protect this system against different spoofing attacks. For this, replayed attack is considered, because of very common accessibility of recording devices. In replay spoofing, the speech utterances of target (genuine) speakers are recorded and played against ASV system for gaining access unauthorizedly. For this purpose, as a first step, different dynamic features will be extracted for each speech sample. For feature extraction MFCC, LFCC, and MGDCC feature extraction techniques are used. As a second step, a classifier is used to classify whether the given speech sample is genuine or not. As a classifier, GMM and universal background model is used. In this present work, GMM based ASV system and Countermeasure systems using different feature extraction techniques are developed, and the performance of the methods is evaluated using EER and t- DCF. Basing on the performance values, the best feature extraction technique is selected.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.