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Convolutional Fusion Network for Face Verification in the Wild

by Chao Xiong, Luoqi Liu, Xiaowei Zhao, Shuicheng Yan, Senior Member, Tae-kyun Kim
"... Abstract—Part-based methods have seen popular applica-tions for face verification in the wild, since they are more robust to local variations in terms of pose, illumination and so on. However, most of the part-based approaches are built on hand-crafted features, which may be not suitable for the spe ..."
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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

L.: Deepface: Closing the gap to human-level performance in face verification

by Yaniv Taigman, Ming Yang, Lior Wolf - In: IEEE CVPR , 2014
"... In modern face recognition, the conventional pipeline consists of four stages: detect ⇒ align ⇒ represent ⇒ clas-sify. We revisit both the alignment step and the representa-tion step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face represe ..."
Abstract - Cited by 103 (4 self) - Add to MetaCart
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

Learning Sparse Face Features: Application to Face Verification

by Pierre Buyssens, Marinette Revenu
"... 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 the rec ..."
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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

Multimodal Priority Verification of Face and Speech Using Momentum Back-Propagation Neural Network

by Changhan Park, Myungseok Ki, Jaechan Namkung, Joonki Paik
"... 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 verificatio ..."
Abstract - Cited by 4 (0 self) - Add to MetaCart
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

Face Detection Using an Adaptive Skin-Color Filter and FMM Neural Networks*

by Ho-joon Kim, Tae-wan Ryu, Juho Lee, Hyun-seung Yang
"... 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 the s ..."
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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

Deeply learned face representations are sparse, selective, and robust

by Yi Sun, Xiaogang Wang, Xiaoou Tang
"... 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 new ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
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

Multi-Modal Biometrics Human Verification using LDA and DFB

by Aloysius George
"... Biometrics is one of the recent trends in security, which is mainly used for verification and recognition systems. By using biometrics we confirm a particular person’s claimed identity based on particular person’s physiological or behavioral characteristics such as fingerprint, face or voice etc. ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
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

Memory Access Optimized Routing Scheme for Deep Networks on a Mobile Coprocessor

by Aysegul Dundar , Jonghoon Jin , Vinayak Gokhale , Berin Martini , Eugenio Culurciello
"... 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 of filte ..."
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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

unknown title

by unknown authors
"... Automatic face recognition in unconstrained environ-ments is a challenging task. To test current trends in face recognition algorithms, we organized an evaluation on face recognition in mobile environment. This paper presents the results of 8 different participants using two verification met-rics. M ..."
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optimal features with a convolutional neural network. Additionally, we assess the usability of the algorithms in mobile devices with limited resources. 1.

SIMULATION, DEVELOPMENT AND DEPLOYMENT OF MOBILE WIRELESS SENSOR NETWORKS FOR MIGRATORY BIRD TRACKING

by Bird Tracking, William P. Bennett, William P., Development, Deployment Of, Mobile Wireless Sensor, William P. Bennett, William P. Bennett, Adviser Mehmet, C. Vuran , 2012
"... 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 reli ..."
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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
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