of following two deliberate components: a Deep Mixture Model (DMM) to find accurate patch correspondence and a Convolutional Fusion Network (CFN) to extract the part based facial features. Specifically, DMM robustly depicts the spatial-appearance distribution of patch features over the faces via several

representation from a nine-layer deep neural network. This deep network involves more than 120 million parameters using several locally connected layers without weight shar-ing, rather than the standard convolutional layers. Thus we trained it on the largest facial dataset to-date, an iden-tity labeled dataset

We present a low resolution face recognition technique based on a Convolutional Neural Network approach. The network is trained to reconstruct a reference–per subject image. In classical feature–based approaches, a first stage of features extraction is followed by a classification to perform

Abstract. In this paper, we propose a priority verification method for multimodal biometric features by using a momentum back-propagation artificial neural network (MBP-ANN). We also propose a personal verification method using both face and speech to improve the rate of single biometric

Abstract. In this paper, we present a real-time face detection method based on hybrid neural networks. We propose a modified version of fuzzy min-max (FMM) neural network for feature analysis and face classification. A relevance factor between features and pattern classes is defined to analyze

This paper designs a high-performance deep convo-lutional network (DeepID2+) for face recognition. It is learned with the identification-verification supervisory signal. By increasing the dimension of hidden repre-sentations and adding supervision to early convolutional layers, DeepID2+ achieves

overcomes the limitation of single biometric system and proves stable personal verification in real-time. Keywords: Biometrics, Multimodal, Face, Fingerprint, Linear Discriminant Analysis, Artificial Neural Network

Abstract-In this paper, we present a memory access optimized routing scheme for a hardware accelerated real-time implementation of deep convolutional neural networks (DCNNs) on a mobile platform. DCNNs consist of multiple layers of 3D convolutions, each comprising between tens and hundreds

optimal features with a convolutional neural network. Additionally, we assess the usability of the algorithms in mobile devices with limited resources. 1.

This thesis presents CraneTracker, a multi-modal sensing and communication system for monitoring migratory species at the continental level. By exploiting the robust and extensive cellular infrastructure across the continent, traditional mobile wireless sensor networks can be extended to enable