Coupled Auto-Associative Neural Networks for Heterogeneous Face Recognition
نویسندگان
چکیده
منابع مشابه
Coupled Deep Learning for Heterogeneous Face Recognition
Heterogeneous face matching is a challenge issue in face recognition due to large domain difference as well as insufficient pairwise images in different modalities during training. This paper proposes a coupled deep learning (CDL) approach for the heterogeneous face matching. CDL seeks a shared feature space in which the heterogeneous face matching problem can be approximately treated as a homo...
متن کاملTrend Detection Using Auto-Associative Neural Networks
In section 2, a definition of “trend” is given. In section 3, it is shown how to detect a trend using an auto-associative neural network. Experimental methods and results are reported in sections 4 and 5, and concluding remarks are given in section 6. Abstract — This paper reports the results of a new neural network based trend detector. An auto-associative neural network was trained with the “...
متن کاملFeature extraction using auto-associative neural networks
Modal analysis is now mature and well accepted in the design of mechanical structures. It determines the vibration mode shapes and the corresponding natural frequencies. However, the validity of modal analysis is limited to structures showing a linear behaviour. In non-linear structural dynamics, it is well known that mode shapes are no longer useful for the characterization of the dynamic resp...
متن کاملKeyword Extraction using Auto-associative Neural Networks
The large amount of textual information digitally available today gives rise to the need for effective means of indexing, searching and retrieving this information.
متن کاملAuto-Associative Neural Networks and Eigenbands Fusion for Frontal Face Verification
Face classification is an important area of research with many applications, including biometric security and searching face databases. This article describes an approach to verify faces using Auto-associative Neural Networks and Eigenbands fusion. In Eigenbands strategy each faces is divided in horizontal bands from which are extracted features using PCA. This method aims capture discriminativ...
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: IEEE Access
سال: 2015
ISSN: 2169-3536
DOI: 10.1109/access.2015.2479620