Deep Periocular Recognition Method via Multi-Angle Data Augmentation
نویسندگان
چکیده
منابع مشابه
Multi-Task Zero-Shot Action Recognition with Prioritised Data Augmentation
Zero-Shot Learning (ZSL) promises to scale visual recognition by bypassing the conventional model training requirement of annotated examples for every category. This is achieved by establishing a mapping connecting low-level features and a semantic description of the label space, referred as visual-semantic mapping, on auxiliary data. Reusing the learned mapping to project target videos into an...
متن کاملData Augmentation Using Multi-Input Multi-Output Source Separation for Deep Neural Network Based Acoustic Modeling
We investigate the use of local Gaussian modeling (LGM) based source separation to improve speech recognition accuracy. Previous studies have shown that the LGM based source separation technique has been successfully applied to the runtime speech enhancement and the speech enhancement of training data for deep neural network (DNN) based acoustic modeling. In this paper, we propose a data augmen...
متن کاملImproving Deep Learning using Generic Data Augmentation
Deep artificial neural networks require a large corpus of training data in order to effectively learn, where collection of such training data is often expensive and laborious. Data augmentation overcomes this issue by artificially inflating the training set with label preserving transformations. Recently there has been extensive use of generic data augmentation to improve Convolutional Neural N...
متن کاملBiometric Recognition Using Periocular Images
We present a new system for biometric recognition using periocular images based on retinotopic sampling grids and Gabor analysis of the local power spectrum at different frequencies and orientations. A number of aspects are studied, including: 1) grid adaptation to dimensions of the target eye vs. grids of constant size, 2) comparison between circularand rectangular-shaped grids, 3) use of Gabo...
متن کاملDeep Convolutional Neural Networks and Data Augmentation for Acoustic Event Recognition
We propose a novel method for Acoustic Event Recognition (AER). In contrast to speech, sounds coming from acoustic events may be produced by a wide variety of sources. Furthermore, distinguishing them often requires analyzing an extended time period due to the lack of a clear sub-word unit. In order to incorporate the long-time frequency structure for AER, we introduce a convolutional neural ne...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Advanced Network, Monitoring and Controls
سال: 2021
ISSN: 2470-8038
DOI: 10.21307/ijanmc-2021-002